Classification is one of the most popular techniques in the data mining area . In supervised learning , a new pattern is assigned a class label based on a training set whose class labels are already known . This paper proposes a novel classification algorithm for time series data . The aim of this work is to generate shadow ratio threshold automatically without human interaction . To alleviate the traditional drawbacks of shadow detection , we fully combine supervised and unsupervised shadow detection method to suggest a self-supervised learning method , which supports us a strongly clue with establishing the relation of shadow and its original object . Subsequently , we benefit from gray-scale histogram to extract shadow segments , then shadow outlines are obtained . This paper presented an unsupervised self-learning method to detect shadows without any priori scene information or threshold parameters . According to the statistical techniques , the method learned scene shadow characteristics actively , so as to achieve the purpose of shadow detection . Training visual and audio networks from scratch , without any additional supervision other than the raw unconstrained videos themselves , is shown to successfully solve this task , and , more interestingly , result in good visual and audio representations . These features set the new state-of-the-art on two sound classification benchmarks , and perform on par with the state-of-the-art selfsupervised approaches on ImageNet classification . We also demonstrate that the network is able to localize objects in both modalities , as well as perform fine-grained recognition tasks . As a result , these algorithms are poorly suited to the task of tracking unstructured roads typical in desert environments . In this paper , we propose a road following algorithm that operates in a selfsupervised learning regime , allowing it to adapt to changing road conditions while making no assumptions about the general structure or appearance of the road surface . An application of optical flow techniques , paired with one-dimensional template matching , allows identification of regions in the current camera image that closely resemble the learned appearance of the road in the recent past . Only short range traversable regions can be detected using traditional methods based on stereo vision because of the limited image resolution and baseline of stereo vision . In this paper , we propose a novel method to detect long range traversable regions without using any supervised or self-supervised learning process . Superpixels are clustered using an improved spectral clustering algorithm to segment the image effectively , and after integrating short range traversable region detection based on u-v-disparity , the traversable region can be extended to long range naturally . But the prediction performance will degenerate very fast as the new point gets away from the convex hull ( extrapolation ) . In this paper , we suggest regarding the prediction not just a mathematical extrapolation , but a mathematics-assisted physical problem , and propose a supervised self-learning scheme , adaptive space transformation ( AST ) , for the prediction . AST tries to automatically detect an underlying invariant relation with the known data under the supervision of physicists . We propose an unsupervised approach to predict depth maps from images captured by a wireless endoscopic capsule . Recent advances in deep learning have shown that accurate depth maps can be predicted from a single image , where the deep network is trained via unsupervised or self-supervised learning by using monocular video sequences or stereo image pairs . However , directly applying these techniques to endoscopic images does not yield satisfactory results owing to the inherent difficulties of the wireless capsule imaging such as dim lighting and low-resolution of images , which are different from normal imaging conditions . This guarantees that the augmented dataset generally supports a better characterization of the class distributions during embedding learning . The second aspect , on the other hand , supports the usage of supervised learning [ reference ] , a different mechanism from [ reference ] which leverages unlabeled GAN images for regularization . section : Baseline Deep Re - ID Model For example , recent works show that the temporal information from videos can be used as a plentiful source of supervision to learn embeddings that are useful for various tasks [ reference ][ reference ] . Other works show that deep features learned from egomotion supervision perform better than features using class - labels as supervision for many tasks [ reference ] . Analogous to these recent works in self - supervised learning , our method of extracting training data and correspondence labels from existing RGB - D reconstructions online is fully automatic , and does not require any manual labor or human supervision . section : Learning From Reconstructions section : Supervised training data for reading comprehension The reading comprehension task naturally lends itself to a formulation as a supervised learning problem . Specifically we seek to estimate the conditional probability , where is a context document , a query relating to that document , and the answer to that query . Secondly , the results on STL - 10 and CIFAR - 10 consistently show that spatial invariance and color - contrast invariance are approximately of equal importance for the classification performance . This indicates that variations in color and contrast , though often neglected , may also improve performance in a supervised learning scenario . Thirdly , on Caltech - 101 color and contrast transformations are much more important compared to spatial transformations than on the two other datasets . section : Discussion We described Paragraph Vector , an unsupervised learning algorithm that learns vector representations for variable - length pieces of texts such as sentences and documents . The vector representations are learned to predict the surrounding words in contexts sampled from the paragraph . During training , we use a loss defined over the transformed image ( rather than the predicted flow ) . This is closely related to so - called self - supervised techniques that learn to predict optical flow and depth from unlabeled video data . Specifically , for the reconstruction tasks we consider such as image super - resolution , the forward degradation process can be easily simulated to generate a large quantity of training data without manual collection or annotation . Several CNN architectures have been developed for image generation . Fully convolutional networks can be trained in the supervised learning manner to generate an image from an input image . The generative CNN model stacks four convolution layers upon five fully connected layers to generate images from object description . This results in a training procedure that is much closer to the test phase , and allows us to directly optimize for a task - specific score such as BLEU . Crucially , since we leverage these techniques in the supervised learning setting rather than the traditional RL setting , we condition the critic network on the ground - truth output . We show that our method leads to improved performance on both a synthetic task , and for German - English machine translation . This suggests that collection of a larger - scale dataset to study visual pretraining may greatly benefit the field . Our findings also suggest a bright future for unsupervised or self - supervised representation learning approaches . It seems the scale of data can overpower noise in the label space . We then define a two-level prosodic annotation of expressive corpora , describing accentual patterns and changes in speaking style . The integration of this manual annotation in the synthesis of sports commentaries positively impacts the naturalness of the expressivity . We also present an automatic annotator of accentual patterns in French and show that its integration in synthesis contributes to the naturalness of the voice . Deep neural network models trained on large labeled datasets are the state-of-the-art in a large variety of computer vision tasks . In many applications , however , labeled data is expensive to obtain or requires a time consuming manual annotation process . In contrast , unlabeled data is often abundant and available in large quantities . A perceptual experiment is designed to evaluate the phrase accent assignment . The result shows that it is important to assign phrase accent for Chinese ITS systems from the perceptual point of view , and that the performance of our automatic method is close to the manually annotation The parameters of annotation include time limit , amplitude and shapes of P , QRS , etc . Compared with the manual annotating method , annotating with computer is more convenient , efficient and accurate . This article presents empirical evaluations of aspects of annotation for the linguistic property of animacy in Swedish , ranging from manual human annotation , automatic classification and , finally , an external evaluation in the task of syntactic parsing . We show that a treatment of animacy as a lexical semantic property of noun types enables generalization over distributional properties of these nouns which proves beneficial in automatic classification and furthermore gives significant improvements in terms of parsing accuracy for Swedish , compared to a state-of-the-art baseline parser with gold standard animacy information . Annotation can be done either manually or automatically . The retrieval performance of the manual annotation based approaches is quite good , as compared to approaches based on automatic annotation . However , manual annotation is time consuming and labor extensive . Thus hashtags have become a victim for spam , fake and un-related advertising content dissemination . In this paper we propose a novel approach designed on 9 distinctive parameters which extends to 4 other derived statistic from Twitter Streaming API , to detect Hashtag hijacking using Neural network analysis which shows a mean hijacking percentage of 28.5 over 10 , 240 test tweets collected whereas , manual based annotation performed results in 17.14 % hijacking . Our method over collected dataset results in 94.025 % accuracy . Researchers annotate ( code ) interview transcriptions , often with the help of Computer-Assisted Qualitative Data Analysis Software ( CAQDAS ) . The tools available today largely replicate the manual process of annotation . In this article , we demonstrate how to use natural language processing ( NLP ) to increase the reproducibility and traceability of the process of applying codes to text data . On these , we intend to build our model of free verse prosody , which will help to understand , differentiate and relate the different styles of free verse poetry . We plan to use our processing scheme on large amounts of data to iteratively build models of styles , to validate and guide manual style annotation , to identify further rhythmical categories , and ultimately to broaden our understanding of free verse poetry . In this paper , we report on a proof-of-concept of our methodology using smaller amounts of poems and a limited set of features . We present an approach of semi-automatic method annotation using differential code coverage , focusing on end-user features . We compare the results of semi-automatic annotation of a sample program with manually performed annotation . In our preliminary implementation , we achieve a precision and recall over 40 % , identify the shortcomings and name differences . The tag Long has been assigned to sentence pairs with a premise containing more than 30 tokens , or a hypothesis with more than 16 tokens . Details about the tags used in the manual annotation are presented in Table [ reference ] . We examined the differences in the tags distributions between the SNLI and SNLI test sets ( Table [ reference ] ) . In this section , we describe three types of output embeddings : human - annotated attributes , unsupervised word embeddings learned from large text corpora , and hierarchical embeddings derived from WordNet . subsection : Embedding by Human Annotation : Attributes Annotating images with class labels is a laborious process when the objects represent fine - grained concepts that are not common in our daily lives . Despite a growing interest in emoji and the hurdle of creating such personal emoji manually , no system has been proposed , to our knowledge , that can solve this problem . Our method is able to produce face emoji that are visually appealing and capture much more of the facial characteristics than the emoji created by well - trained human annotators who use the conventional tools . section : Related work paragraph : Self - Supervision Though the proposed framework for training Predictive Filter Flow requires paired inputs and target outputs , we note that generating training data for many reconstruction tasks can be accomplished automatically without manual labeling . Given a pool of high quality images , we can automatically generate low - resolution , blurred or JPEG degraded counterparts to use in training ( see Section [ reference ] ) . In many human activity recognition systems the size of the unlabeled training data may be significantly large due to expensive human effort required for data annotation . Moreover , the insufficient data collection process from heterogenous sources may cause dissimilarities between training and testing data . Visual Correlation 8 . Visual correlation methods and models , 9.Iconic approach for data annotating , searching and correlating , 10 . Bruegel iconic correlation system PART 4 . Manually annotating large video and digital databases of driving behavior is costly and time-consuming . This paper will discuss a data annotation tool that automates the process and reduces the number of man-hours required to annotate data . The laboratory has utilized this tool on a large database of simulated driving data to develop context aware driving systems . First , we provide a novel solution for fast near-duplicate video retrieval . Second , based on the outcome of near-duplicate search , we explore the potential that the data-driven annotation could be successful when huge volume of tagged web videos is freely accessible online . Experiments on cross sources ( annotating Google videos and Yahoo ! To address these limitations , in this work we extend W4 M to help scientists in the definition and in the formatting of the whole collection of metadata coming from metabolomics based projects . For this purpose , we included new tools to acquire and transform in an user-friendly way metabolomics experimental raw data , study features and data analysis annotation , with the aim of assisting biologist in the incorporation of the minimal information for an experiment . This work will also generalize the use of the standard mzML format in W4 M and the ISA Tab format for its posterior automatic submission ta MetaboLights , enhancing in this way the link between data providers and data managers . In machine learning applications , practitioners must take into account the cost associated with the algorithm . These costs include : Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting , validation , and testing Cost of collecting features/attributes for test data Cost of user feedback collection Cost of incorrect prediction/classification Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area . It discusses real-world applications that incorporate the cost of learning into the modeling process . The present paper builds upon and extends that work by adding arbitrary constraints and multiple levels of hierarchy in the concept representation model . We consider queries simply as descriptions of virtual data set , and that allows us to use the same unifying concept representation for query specification , as well as for data annotation purposes . We also identify some key issues and challenges presented by the new framework , and we outline possible approaches for overcoming them . ( A ) a step of capturing the original image ( 2 ) originating user is using a mobile device ( 1 ) ; ( b ) an image ( 21 ) , the originating user mobile device together with the first annotation ( 210 ) ( 1 ) steps and are displayed above ; ( c ) said first first data information annotation including location and time of the mobile device together with information that is displayed ( R1 ) is sent to the server ( 3 ) a step for : ( d ) transmitting the user , an image and transmitting to at least one second user of the second device ( 4 ) ; display ( e ) image ( 22 ) on said second device a step which is ; ( f ) second annotation ( 211 ) has steps and displayed on the second user is combined with the image ; ( g ) said second Anote A step distribution ( 211 ) a second data information including the location and time of the in conjunction with information that is displayed second user equipment ( 1 ' ) ( R2 ) is sent to the server ( 3 ) ; ( h ) the server ( 3 ) is , from the location and / or time , whether the image is an original image captured by the originating user , or the transmission image that is displayed to the second user method comprising the steps of determining . .The The HUPO Proteomics Standards Initiative ( PSI ) defines standards for data representation in proteomics to facilitate data exchange and comparison , and quality assessment . A set of minimum reporting requirements , called MIAPE ( for Minimum Information About a Proteomics Experiment ) is provided to ensure consistency of data set annotation . Like the MIAME reporting requirements for transcriptomics , it is anticipated that journal editors will soon require such annotation for published data sets , simplifying further mining of data . section : Well - formed Natural Language Question Classifier In this section we describe the data annotation , and the models used for question well - formedness classification . subsection : Dataset Construction In this paper we describe a new QPR system implemented as a Neural Information Retrieval ( NIR ) system consisting of a neural network sentence encoder and an approximate k-Nearest Neighbour index for efficient vector retrieval . We also describe our mechanism to generate an annotated dataset for question paraphrase retrieval experiments automatically from question-answer logs via distant supervision . We show that the standard loss function in NIR , triplet loss , does not perform well with noisy labels . This paper presents a flexible NER framework compatible with different languages and domains . Inspired by the idea of distant supervision ( DS ) , this paper enhances the representation by increasing the entity-context diversity without relying on external resources . We choose different layer stacks and sub-network combinations to construct the bilateral networks . Fine-grained entity type classification ( FETC ) is the task of classifying an entity mention to a broad set of types . Distant supervision paradigm is extensively used to generate training data for this task . However , generated training data assigns same set of labels to every mention of an entity without considering its local context . Our model achieves an improvement of 19.7 % for classification accuracy and 20.1 % for micro F1 measure over the current state-of-the-art . We eliminate the need for manual labeling by employing a distant supervision based method to obtain labeled examples for training the models . We thoroughly analyze our model to discover cues that are indicative of a particular persona . Compared to the previous supervised models , our approach does not require human efforts such as annotation and labeling , which is its advantage in trending big data applications . Meanwhile , the distant supervision combination allows RelTM to incorporate rich existing knowledge resources provided by domain experts . The experimental results on the 2013 DDI challenge corpus reach 48 % in F1 score , showing the effectiveness of RelTM . Instance-popularity is to encode the strong relevance of time and true relation mention . Therefore , instance-popularity would be an effective clue to reduce the noises generated through distant supervision labeling . The two strategies , i.e. , hard filter and curriculum learning are both ways to implement instance-popularity for better relation extraction in the manner of Time-DS . In this paper , we propose to train neural ranking models using weak supervision , which is the most natural way to reuse the existing supervised learning models where the imperfect labels are treated as the ground truth . The basic assumption is that we can cheaply obtain labels ( that are of lower quality than human - provided labels ) by expressing the prior knowledge we have about the task at hand by specifying a set of heuristics , adapting existing ground truth data for a different but related task ( this is often referred to distant supervision ) , extracting supervision signal from external knowledge - bases or ontologies , crowd - sourcing partial annotations that are cheaper to get , etc . Weak supervision is a natural way to benefit from unsupervised data and it has been applied in NLP for various tasks including relation extraction Bing:2015 , Han:2016 , knowledge - base completion Hoffmann:2011 , sentiment analysis Severyn:2015 , etc . Because the evidence is gathered by an automated process , the documents are not guaranteed to contain all facts needed to answer the question . In addition to distant supervision evaluation , we also evaluate models on a verified subsets . Because the test set is not released , we train our model on training set and evaluate our model on dev set . The authors have extended the paradigm of machine learning algorithms to the related problem of bat call detection . A robust automatic bat call detection algorithm , to replace hand labeling , is required for two reasons : ( 1 ) for real‐time species identification in the field , and ( 2 ) because hand labeling is subjective , tedious , slow , and error‐prone . The current experiments compare various frame‐based features ( log energy , pitch estimates , pitch slopes ) with several models of detection ( matched filters , Gaussian mixtures , d ... In this paper , we describe audiovisual automatic speech recognition experiments carried using visual parameters extracted from “ natural ” images . Unlike many other experiments in the AV ASR field , these visual parameters are obtained without any hand-labeling phase and are naturally noisy , due to the extraction process . We evaluate our models with different strategies among which : use of a shape model combined with or after an appearance model . Given a sentence that describes an image , the authors first used a parser to parse the sentence , then replaced the key word in the sentence using question words and the key word became the answer . created an image QA data set through human labeling . The initial version was in Chinese and then was translated to English . Finally , humans manually validated , corrected , and discarded noisy frames . On average , the human labeling procedure took one second per frame . section : Experiments Inspired by the recent success of deep latent variable models we parameterize rounding procedures as a neural network , which lends itself to efficient optimization via back-propagation . By minimizing the expected value of the objective of the discrete labeling problem over training samples , we learn a rounding procedure that is more suited to the task at hand . Using both synthetic and real world data sets , we demonstrate that our approach can outperform the state-of-the-art hand-designed rounding procedures . Such a term is defined in the spatiotemporal domain , where the ventricular wall motion is introduced to account for correspondences between the consecutive segmentation maps . The estimation of the deformations is done through a continuous deformation field ( FFD ) where the displacements of the control points are determined using discrete labeling approach . Principles from linear programming and in particular the Primal/Dual Schema is used to recover the optimal solution in both spaces . An Efficient Non-parametric Random Field Model for Image Labeling . This chapter introduces a new random field model for discrete image labeling tasks , the Decision Tree Field ( DTF ) , that combines and generalizes decision forests and conditional random fields ( CRF ) which have been widely used in computer vision . Previous research works have proved that it is either a NP-Complete or a co-NP complete problem . In this paper , we propose DLS ( Discrete Label Set ) , a novel subscription representation model , and design algorithms to achieve the mapping from traditional Boolean predicate model to the DLS model . Based on the DLS model , we propose a subscription aggregation algorithm with O(1 ) time complexity in most cases , and an event matching algorithm with O(1 ) time complexity . Learning short binary codes is challenged by the inherent discrete nature of the problem . The graph cuts algorithm is a well-studied discrete label assignment solution in computer vision , but has not yet been applied to solve the binary coding problems . This is partially because it was unclear how to use it to learn the encoding ( hashing ) functions for out-of-sample generalization . A paradigm that has evolved for dealing with this problem is data programming . An existing data programming paradigm allows human supervision to be provided as a set of discrete labeling functions ( LF ) that output possibly noisy labels to input instances and a generative modelfor consolidating the weak labels . We enhance and generalize this paradigm by supporting functions that output a continuous score ( instead of a hard label ) that noisily correlates with labels . Our loss function simply enforces the constraint that the embedding vectors used to label different instances are far apart . Since neither the number of labels , nor the target label vectors are specified in advance , we ca n’t use standard soft - max thresholding to produce a discrete labeling . Instead , we utilize a variant of mean - shift clustering which can be viewed as a recurrent network whose fixed point identifies a small , discrete set of instance label vectors and concurrently labels each pixel with one of the vectors from this set . Nonetheless , how to effectively remove noisy labels from a training set with mislabeled samples is a nontrivial task for a multitude of supervised classification methods in HSI processing . This paper is the first to propose a kernel entropy component analysis (KECA)-based method for noisy label detection that can remove noisy labels of a training set with mislabeled samples and improve performance of supervised classification in HSI , which consists of the following steps . First , the kernel matrix of training samples with noisy labels for each class can be achieved by exploiting a nonlinear mapping function to enlarge the sample separability . To address the challenge of automatically labeling large-scale training data , a distant supervision strategy is introduced to relation extraction by heuristically aligning entity pairs in plain text with the knowledge base . Unfortunately , the method is vulnerable to the noisy label problem due to the incompletion of the exploited knowledge base . Existing works focus on the specific algorithms , but few works summarize the commonalities between different methods and the influencing factors of these denoising mechanisms . However , an inherent issue with this approach is label imperfections in training data , since the line of demarcation between classes is determined based on field expert experience and maintenance capability . To address this issue we propose a noisy-label model in which the labeling noise function is derived from a point of view motivated by reliability analysis . We also present a novel label bootstrapping method that can better reflect the true uncertainty of the labeling process than the standard approach for addressing label imperfections . Our controlled experimental evaluation shows that the adaptive system yields higher global performance than two non-adaptive control systems , but the difference is only significant for a subset of students . Our system error analysis indicates that noisy affect labeling is a major performance bottleneck , yielding fewer than expected adaptations thus lower than expected performance . However , the percentage of received adaptation correlates with higher performance over all students . A Study on Noisy Label Supervision . Semi-supervised clustering algorithms aim at discovering the hidden structure of data sets with the help of expert knowledge , generally expressed as constraints on the data such as class labels or pairwise relations . improving data quality and data mining using multiple , noisy labelers . This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect . On the 100 clean utterances both models perform about the same , 9.2 % WER and 9.0 % WER for the clean trained model and the noise trained model respectively . However , on the 100 noisy utterances the noisy model achieves 22.6 % WER over the clean model ’s 28.7 % WER , a 6.1 % absolute and 21.3 % relative improvement . section : Related Work In this section we propose an approach to training pipelined question answering systems , where a single paragraph is heuristically extracted from the context document ( s ) and passed to a paragraph - level QA model . We suggest using a TF - IDF based paragraph selection method and argue that a summed objective function should be used to handle noisy supervision . We also propose a refined model that incorporates some recent modeling ideas for reading comprehension systems .
Classification is one of the most popular techniques in the data mining area . In supervised learning , a new pattern is assigned a class label based on a training set whose class labels are already known . This paper proposes a novel classification algorithm for time series data .
The aim of this work is to generate shadow ratio threshold automatically without human interaction . To alleviate the traditional drawbacks of shadow detection , we fully combine supervised and unsupervised shadow detection method to suggest a self-supervised learning method , which supports us a strongly clue with establishing the relation of shadow and its original object . Subsequently , we benefit from gray-scale histogram to extract shadow segments , then shadow outlines are obtained .
This paper presented an unsupervised self-learning method to detect shadows without any priori scene information or threshold parameters . According to the statistical techniques , the method learned scene shadow characteristics actively , so as to achieve the purpose of shadow detection .
Training visual and audio networks from scratch , without any additional supervision other than the raw unconstrained videos themselves , is shown to successfully solve this task , and , more interestingly , result in good visual and audio representations . These features set the new state-of-the-art on two sound classification benchmarks , and perform on par with the state-of-the-art selfsupervised approaches on ImageNet classification . We also demonstrate that the network is able to localize objects in both modalities , as well as perform fine-grained recognition tasks .
As a result , these algorithms are poorly suited to the task of tracking unstructured roads typical in desert environments . In this paper , we propose a road following algorithm that operates in a selfsupervised learning regime , allowing it to adapt to changing road conditions while making no assumptions about the general structure or appearance of the road surface . An application of optical flow techniques , paired with one-dimensional template matching , allows identification of regions in the current camera image that closely resemble the learned appearance of the road in the recent past .
Only short range traversable regions can be detected using traditional methods based on stereo vision because of the limited image resolution and baseline of stereo vision . In this paper , we propose a novel method to detect long range traversable regions without using any supervised or self-supervised learning process . Superpixels are clustered using an improved spectral clustering algorithm to segment the image effectively , and after integrating short range traversable region detection based on u-v-disparity , the traversable region can be extended to long range naturally .
But the prediction performance will degenerate very fast as the new point gets away from the convex hull ( extrapolation ) . In this paper , we suggest regarding the prediction not just a mathematical extrapolation , but a mathematics-assisted physical problem , and propose a supervised self-learning scheme , adaptive space transformation ( AST ) , for the prediction . AST tries to automatically detect an underlying invariant relation with the known data under the supervision of physicists .
We propose an unsupervised approach to predict depth maps from images captured by a wireless endoscopic capsule . Recent advances in deep learning have shown that accurate depth maps can be predicted from a single image , where the deep network is trained via unsupervised or self-supervised learning by using monocular video sequences or stereo image pairs . However , directly applying these techniques to endoscopic images does not yield satisfactory results owing to the inherent difficulties of the wireless capsule imaging such as dim lighting and low-resolution of images , which are different from normal imaging conditions .
This guarantees that the augmented dataset generally supports a better characterization of the class distributions during embedding learning . The second aspect , on the other hand , supports the usage of supervised learning [ reference ] , a different mechanism from [ reference ] which leverages unlabeled GAN images for regularization . section : Baseline Deep Re - ID Model
For example , recent works show that the temporal information from videos can be used as a plentiful source of supervision to learn embeddings that are useful for various tasks [ reference ][ reference ] . Other works show that deep features learned from egomotion supervision perform better than features using class - labels as supervision for many tasks [ reference ] . Analogous to these recent works in self - supervised learning , our method of extracting training data and correspondence labels from existing RGB - D reconstructions online is fully automatic , and does not require any manual labor or human supervision . section : Learning From Reconstructions
section : Supervised training data for reading comprehension The reading comprehension task naturally lends itself to a formulation as a supervised learning problem . Specifically we seek to estimate the conditional probability , where is a context document , a query relating to that document , and the answer to that query .
Secondly , the results on STL - 10 and CIFAR - 10 consistently show that spatial invariance and color - contrast invariance are approximately of equal importance for the classification performance . This indicates that variations in color and contrast , though often neglected , may also improve performance in a supervised learning scenario . Thirdly , on Caltech - 101 color and contrast transformations are much more important compared to spatial transformations than on the two other datasets .
section : Discussion We described Paragraph Vector , an unsupervised learning algorithm that learns vector representations for variable - length pieces of texts such as sentences and documents . The vector representations are learned to predict the surrounding words in contexts sampled from the paragraph .
During training , we use a loss defined over the transformed image ( rather than the predicted flow ) . This is closely related to so - called self - supervised techniques that learn to predict optical flow and depth from unlabeled video data . Specifically , for the reconstruction tasks we consider such as image super - resolution , the forward degradation process can be easily simulated to generate a large quantity of training data without manual collection or annotation .
Several CNN architectures have been developed for image generation . Fully convolutional networks can be trained in the supervised learning manner to generate an image from an input image . The generative CNN model stacks four convolution layers upon five fully connected layers to generate images from object description .
This results in a training procedure that is much closer to the test phase , and allows us to directly optimize for a task - specific score such as BLEU . Crucially , since we leverage these techniques in the supervised learning setting rather than the traditional RL setting , we condition the critic network on the ground - truth output . We show that our method leads to improved performance on both a synthetic task , and for German - English machine translation .
This suggests that collection of a larger - scale dataset to study visual pretraining may greatly benefit the field . Our findings also suggest a bright future for unsupervised or self - supervised representation learning approaches . It seems the scale of data can overpower noise in the label space .
We then define a two-level prosodic annotation of expressive corpora , describing accentual patterns and changes in speaking style . The integration of this manual annotation in the synthesis of sports commentaries positively impacts the naturalness of the expressivity . We also present an automatic annotator of accentual patterns in French and show that its integration in synthesis contributes to the naturalness of the voice .
Deep neural network models trained on large labeled datasets are the state-of-the-art in a large variety of computer vision tasks . In many applications , however , labeled data is expensive to obtain or requires a time consuming manual annotation process . In contrast , unlabeled data is often abundant and available in large quantities .
A perceptual experiment is designed to evaluate the phrase accent assignment . The result shows that it is important to assign phrase accent for Chinese ITS systems from the perceptual point of view , and that the performance of our automatic method is close to the manually annotation
The parameters of annotation include time limit , amplitude and shapes of P , QRS , etc . Compared with the manual annotating method , annotating with computer is more convenient , efficient and accurate .
This article presents empirical evaluations of aspects of annotation for the linguistic property of animacy in Swedish , ranging from manual human annotation , automatic classification and , finally , an external evaluation in the task of syntactic parsing . We show that a treatment of animacy as a lexical semantic property of noun types enables generalization over distributional properties of these nouns which proves beneficial in automatic classification and furthermore gives significant improvements in terms of parsing accuracy for Swedish , compared to a state-of-the-art baseline parser with gold standard animacy information .
Annotation can be done either manually or automatically . The retrieval performance of the manual annotation based approaches is quite good , as compared to approaches based on automatic annotation . However , manual annotation is time consuming and labor extensive .
Thus hashtags have become a victim for spam , fake and un-related advertising content dissemination . In this paper we propose a novel approach designed on 9 distinctive parameters which extends to 4 other derived statistic from Twitter Streaming API , to detect Hashtag hijacking using Neural network analysis which shows a mean hijacking percentage of 28.5 over 10 , 240 test tweets collected whereas , manual based annotation performed results in 17.14 % hijacking . Our method over collected dataset results in 94.025 % accuracy .
Researchers annotate ( code ) interview transcriptions , often with the help of Computer-Assisted Qualitative Data Analysis Software ( CAQDAS ) . The tools available today largely replicate the manual process of annotation . In this article , we demonstrate how to use natural language processing ( NLP ) to increase the reproducibility and traceability of the process of applying codes to text data .
On these , we intend to build our model of free verse prosody , which will help to understand , differentiate and relate the different styles of free verse poetry . We plan to use our processing scheme on large amounts of data to iteratively build models of styles , to validate and guide manual style annotation , to identify further rhythmical categories , and ultimately to broaden our understanding of free verse poetry . In this paper , we report on a proof-of-concept of our methodology using smaller amounts of poems and a limited set of features .
We present an approach of semi-automatic method annotation using differential code coverage , focusing on end-user features . We compare the results of semi-automatic annotation of a sample program with manually performed annotation . In our preliminary implementation , we achieve a precision and recall over 40 % , identify the shortcomings and name differences .
The tag Long has been assigned to sentence pairs with a premise containing more than 30 tokens , or a hypothesis with more than 16 tokens . Details about the tags used in the manual annotation are presented in Table [ reference ] . We examined the differences in the tags distributions between the SNLI and SNLI test sets ( Table [ reference ] ) .
In this section , we describe three types of output embeddings : human - annotated attributes , unsupervised word embeddings learned from large text corpora , and hierarchical embeddings derived from WordNet . subsection : Embedding by Human Annotation : Attributes Annotating images with class labels is a laborious process when the objects represent fine - grained concepts that are not common in our daily lives .
Despite a growing interest in emoji and the hurdle of creating such personal emoji manually , no system has been proposed , to our knowledge , that can solve this problem . Our method is able to produce face emoji that are visually appealing and capture much more of the facial characteristics than the emoji created by well - trained human annotators who use the conventional tools . section : Related work
paragraph : Self - Supervision Though the proposed framework for training Predictive Filter Flow requires paired inputs and target outputs , we note that generating training data for many reconstruction tasks can be accomplished automatically without manual labeling . Given a pool of high quality images , we can automatically generate low - resolution , blurred or JPEG degraded counterparts to use in training ( see Section [ reference ] ) .
In many human activity recognition systems the size of the unlabeled training data may be significantly large due to expensive human effort required for data annotation . Moreover , the insufficient data collection process from heterogenous sources may cause dissimilarities between training and testing data .
Visual Correlation 8 . Visual correlation methods and models , 9.Iconic approach for data annotating , searching and correlating , 10 . Bruegel iconic correlation system PART 4 .
Manually annotating large video and digital databases of driving behavior is costly and time-consuming . This paper will discuss a data annotation tool that automates the process and reduces the number of man-hours required to annotate data . The laboratory has utilized this tool on a large database of simulated driving data to develop context aware driving systems .
First , we provide a novel solution for fast near-duplicate video retrieval . Second , based on the outcome of near-duplicate search , we explore the potential that the data-driven annotation could be successful when huge volume of tagged web videos is freely accessible online . Experiments on cross sources ( annotating Google videos and Yahoo !
To address these limitations , in this work we extend W4 M to help scientists in the definition and in the formatting of the whole collection of metadata coming from metabolomics based projects . For this purpose , we included new tools to acquire and transform in an user-friendly way metabolomics experimental raw data , study features and data analysis annotation , with the aim of assisting biologist in the incorporation of the minimal information for an experiment . This work will also generalize the use of the standard mzML format in W4 M and the ISA Tab format for its posterior automatic submission ta MetaboLights , enhancing in this way the link between data providers and data managers .
In machine learning applications , practitioners must take into account the cost associated with the algorithm . These costs include : Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting , validation , and testing Cost of collecting features/attributes for test data Cost of user feedback collection Cost of incorrect prediction/classification Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area . It discusses real-world applications that incorporate the cost of learning into the modeling process .
The present paper builds upon and extends that work by adding arbitrary constraints and multiple levels of hierarchy in the concept representation model . We consider queries simply as descriptions of virtual data set , and that allows us to use the same unifying concept representation for query specification , as well as for data annotation purposes . We also identify some key issues and challenges presented by the new framework , and we outline possible approaches for overcoming them .
( A ) a step of capturing the original image ( 2 ) originating user is using a mobile device ( 1 ) ; ( b ) an image ( 21 ) , the originating user mobile device together with the first annotation ( 210 ) ( 1 ) steps and are displayed above ; ( c ) said first first data information annotation including location and time of the mobile device together with information that is displayed ( R1 ) is sent to the server ( 3 ) a step for : ( d ) transmitting the user , an image and transmitting to at least one second user of the second device ( 4 ) ; display ( e ) image ( 22 ) on said second device a step which is ; ( f ) second annotation ( 211 ) has steps and displayed on the second user is combined with the image ; ( g ) said second Anote A step distribution ( 211 ) a second data information including the location and time of the in conjunction with information that is displayed second user equipment ( 1 ' ) ( R2 ) is sent to the server ( 3 ) ; ( h ) the server ( 3 ) is , from the location and / or time , whether the image is an original image captured by the originating user , or the transmission image that is displayed to the second user method comprising the steps of determining . .The
The HUPO Proteomics Standards Initiative ( PSI ) defines standards for data representation in proteomics to facilitate data exchange and comparison , and quality assessment . A set of minimum reporting requirements , called MIAPE ( for Minimum Information About a Proteomics Experiment ) is provided to ensure consistency of data set annotation . Like the MIAME reporting requirements for transcriptomics , it is anticipated that journal editors will soon require such annotation for published data sets , simplifying further mining of data .
section : Well - formed Natural Language Question Classifier In this section we describe the data annotation , and the models used for question well - formedness classification . subsection : Dataset Construction
In this paper we describe a new QPR system implemented as a Neural Information Retrieval ( NIR ) system consisting of a neural network sentence encoder and an approximate k-Nearest Neighbour index for efficient vector retrieval . We also describe our mechanism to generate an annotated dataset for question paraphrase retrieval experiments automatically from question-answer logs via distant supervision . We show that the standard loss function in NIR , triplet loss , does not perform well with noisy labels .
This paper presents a flexible NER framework compatible with different languages and domains . Inspired by the idea of distant supervision ( DS ) , this paper enhances the representation by increasing the entity-context diversity without relying on external resources . We choose different layer stacks and sub-network combinations to construct the bilateral networks .
Fine-grained entity type classification ( FETC ) is the task of classifying an entity mention to a broad set of types . Distant supervision paradigm is extensively used to generate training data for this task . However , generated training data assigns same set of labels to every mention of an entity without considering its local context .
Our model achieves an improvement of 19.7 % for classification accuracy and 20.1 % for micro F1 measure over the current state-of-the-art . We eliminate the need for manual labeling by employing a distant supervision based method to obtain labeled examples for training the models . We thoroughly analyze our model to discover cues that are indicative of a particular persona .
Compared to the previous supervised models , our approach does not require human efforts such as annotation and labeling , which is its advantage in trending big data applications . Meanwhile , the distant supervision combination allows RelTM to incorporate rich existing knowledge resources provided by domain experts . The experimental results on the 2013 DDI challenge corpus reach 48 % in F1 score , showing the effectiveness of RelTM .
Instance-popularity is to encode the strong relevance of time and true relation mention . Therefore , instance-popularity would be an effective clue to reduce the noises generated through distant supervision labeling . The two strategies , i.e. , hard filter and curriculum learning are both ways to implement instance-popularity for better relation extraction in the manner of Time-DS .
In this paper , we propose to train neural ranking models using weak supervision , which is the most natural way to reuse the existing supervised learning models where the imperfect labels are treated as the ground truth . The basic assumption is that we can cheaply obtain labels ( that are of lower quality than human - provided labels ) by expressing the prior knowledge we have about the task at hand by specifying a set of heuristics , adapting existing ground truth data for a different but related task ( this is often referred to distant supervision ) , extracting supervision signal from external knowledge - bases or ontologies , crowd - sourcing partial annotations that are cheaper to get , etc . Weak supervision is a natural way to benefit from unsupervised data and it has been applied in NLP for various tasks including relation extraction Bing:2015 , Han:2016 , knowledge - base completion Hoffmann:2011 , sentiment analysis Severyn:2015 , etc .
Because the evidence is gathered by an automated process , the documents are not guaranteed to contain all facts needed to answer the question . In addition to distant supervision evaluation , we also evaluate models on a verified subsets . Because the test set is not released , we train our model on training set and evaluate our model on dev set .
The authors have extended the paradigm of machine learning algorithms to the related problem of bat call detection . A robust automatic bat call detection algorithm , to replace hand labeling , is required for two reasons : ( 1 ) for real‐time species identification in the field , and ( 2 ) because hand labeling is subjective , tedious , slow , and error‐prone . The current experiments compare various frame‐based features ( log energy , pitch estimates , pitch slopes ) with several models of detection ( matched filters , Gaussian mixtures , d ...
In this paper , we describe audiovisual automatic speech recognition experiments carried using visual parameters extracted from “ natural ” images . Unlike many other experiments in the AV ASR field , these visual parameters are obtained without any hand-labeling phase and are naturally noisy , due to the extraction process . We evaluate our models with different strategies among which : use of a shape model combined with or after an appearance model .
Given a sentence that describes an image , the authors first used a parser to parse the sentence , then replaced the key word in the sentence using question words and the key word became the answer . created an image QA data set through human labeling . The initial version was in Chinese and then was translated to English .
Finally , humans manually validated , corrected , and discarded noisy frames . On average , the human labeling procedure took one second per frame . section : Experiments
Inspired by the recent success of deep latent variable models we parameterize rounding procedures as a neural network , which lends itself to efficient optimization via back-propagation . By minimizing the expected value of the objective of the discrete labeling problem over training samples , we learn a rounding procedure that is more suited to the task at hand . Using both synthetic and real world data sets , we demonstrate that our approach can outperform the state-of-the-art hand-designed rounding procedures .
Such a term is defined in the spatiotemporal domain , where the ventricular wall motion is introduced to account for correspondences between the consecutive segmentation maps . The estimation of the deformations is done through a continuous deformation field ( FFD ) where the displacements of the control points are determined using discrete labeling approach . Principles from linear programming and in particular the Primal/Dual Schema is used to recover the optimal solution in both spaces .
An Efficient Non-parametric Random Field Model for Image Labeling . This chapter introduces a new random field model for discrete image labeling tasks , the Decision Tree Field ( DTF ) , that combines and generalizes decision forests and conditional random fields ( CRF ) which have been widely used in computer vision .
Previous research works have proved that it is either a NP-Complete or a co-NP complete problem . In this paper , we propose DLS ( Discrete Label Set ) , a novel subscription representation model , and design algorithms to achieve the mapping from traditional Boolean predicate model to the DLS model . Based on the DLS model , we propose a subscription aggregation algorithm with O(1 ) time complexity in most cases , and an event matching algorithm with O(1 ) time complexity .
Learning short binary codes is challenged by the inherent discrete nature of the problem . The graph cuts algorithm is a well-studied discrete label assignment solution in computer vision , but has not yet been applied to solve the binary coding problems . This is partially because it was unclear how to use it to learn the encoding ( hashing ) functions for out-of-sample generalization .
A paradigm that has evolved for dealing with this problem is data programming . An existing data programming paradigm allows human supervision to be provided as a set of discrete labeling functions ( LF ) that output possibly noisy labels to input instances and a generative modelfor consolidating the weak labels . We enhance and generalize this paradigm by supporting functions that output a continuous score ( instead of a hard label ) that noisily correlates with labels .
Our loss function simply enforces the constraint that the embedding vectors used to label different instances are far apart . Since neither the number of labels , nor the target label vectors are specified in advance , we ca n’t use standard soft - max thresholding to produce a discrete labeling . Instead , we utilize a variant of mean - shift clustering which can be viewed as a recurrent network whose fixed point identifies a small , discrete set of instance label vectors and concurrently labels each pixel with one of the vectors from this set .
Nonetheless , how to effectively remove noisy labels from a training set with mislabeled samples is a nontrivial task for a multitude of supervised classification methods in HSI processing . This paper is the first to propose a kernel entropy component analysis (KECA)-based method for noisy label detection that can remove noisy labels of a training set with mislabeled samples and improve performance of supervised classification in HSI , which consists of the following steps . First , the kernel matrix of training samples with noisy labels for each class can be achieved by exploiting a nonlinear mapping function to enlarge the sample separability .
To address the challenge of automatically labeling large-scale training data , a distant supervision strategy is introduced to relation extraction by heuristically aligning entity pairs in plain text with the knowledge base . Unfortunately , the method is vulnerable to the noisy label problem due to the incompletion of the exploited knowledge base . Existing works focus on the specific algorithms , but few works summarize the commonalities between different methods and the influencing factors of these denoising mechanisms .
However , an inherent issue with this approach is label imperfections in training data , since the line of demarcation between classes is determined based on field expert experience and maintenance capability . To address this issue we propose a noisy-label model in which the labeling noise function is derived from a point of view motivated by reliability analysis . We also present a novel label bootstrapping method that can better reflect the true uncertainty of the labeling process than the standard approach for addressing label imperfections .
Our controlled experimental evaluation shows that the adaptive system yields higher global performance than two non-adaptive control systems , but the difference is only significant for a subset of students . Our system error analysis indicates that noisy affect labeling is a major performance bottleneck , yielding fewer than expected adaptations thus lower than expected performance . However , the percentage of received adaptation correlates with higher performance over all students .
A Study on Noisy Label Supervision . Semi-supervised clustering algorithms aim at discovering the hidden structure of data sets with the help of expert knowledge , generally expressed as constraints on the data such as class labels or pairwise relations .
improving data quality and data mining using multiple , noisy labelers . This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect .
On the 100 clean utterances both models perform about the same , 9.2 % WER and 9.0 % WER for the clean trained model and the noise trained model respectively . However , on the 100 noisy utterances the noisy model achieves 22.6 % WER over the clean model ’s 28.7 % WER , a 6.1 % absolute and 21.3 % relative improvement . section : Related Work
In this section we propose an approach to training pipelined question answering systems , where a single paragraph is heuristically extracted from the context document ( s ) and passed to a paragraph - level QA model . We suggest using a TF - IDF based paragraph selection method and argue that a summed objective function should be used to handle noisy supervision . We also propose a refined model that incorporates some recent modeling ideas for reading comprehension systems .
Weighted feature-based classification of time series data A Self-Supervised Learning Method for Shadow Detection in Remote Sensing Imagery Self-learning method for shadow detection Look, Listen and Learn Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow Long range traversable region detection based on superpixels clustering for mobile robots Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles Learning Depth from Endoscopic Images Camera Style Adaptation for Person Re-identification 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Teaching Machines to Read and Comprehend Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Distributed Representations of Sentences and Documents Image Reconstruction with Predictive Filter Flow Deep Identity-aware Transfer of Facial Attributes An Actor-Critic Algorithm for Sequence Prediction Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Expressive speech synthesis : research and system design with hidden Markov models Hybrid VAE: Improving Deep Generative Models using Partial Observations Assigning phrase accent to Chinese Text-to-Speech system Waveform Annotation of ECG Database Using Computer Empirical Evaluations of Animacy Annotation Generic Multimedia Database Architecture Based upon Semantic Libraries HashMiner: Feature Characterisation and analysis of #Hashtag Hijacking using real-time neural network Supporting Interview Analysis with Autocoding Large-scale Analysis of Spoken Free-verse Poetry Semi-automatic concern annotation using differential code coverage Grounded Textual Entailment Evaluation of output embeddings for fine-grained image classification Unsupervised Cross-Domain Image Generation Image Reconstruction with Predictive Filter Flow Active privileged learning of human activities from weakly labeled samples Visual and spatial analysis : advances in data mining, reasoning, and problem solving A Semi-Automatic Data Annotation Tool for Driving Simulator Data Reduction On the Annotation of Web Videos by Efficient Near-Duplicate Search Metabolomics resources in interoperability and Open-Science Cost-Sensitive Machine Learning Constrained querying of multimedia databases: issues and approaches Image annotation method and system MIAPEGelDB, a web-based submission tool and public repository for MIAPE gel electrophoresis documents. Identifying Well-formed Natural Language Questions Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings A distant supervision based approach to medical persona classification A Topic-modeling Based Framework for Drug-drug Interaction Classification from Biomedical Text Towards Time-Aware Distant Supervision for Relation Extraction Neural Ranking Models with Weak Supervision MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension Automatic detection of microchiroptera echolocation calls from field recordings using machine learning algorithms Lip-reading based on a fully automatic statistical model Stacked Attention Networks for Image Question Answering Towards Viewpoint Invariant 3D Human Pose Estimation Learning to Round for Discrete Labeling Problems 4D Ventricular Segmentation and Wall Motion Estimation Using Efficient Discrete Optimization Decision Tree Fields: An Efficient Non-parametric Random Field Model for Image Labeling Towards Scalable Subscription Aggregation and Real Time Event Matching in a Large-Scale Content-Based Network Graph Cuts for Supervised Binary Coding Data Programming using Continuous and Quality-Guided Labeling Functions Recurrent Pixel Embedding for Instance Grouping Kernel Entropy Component Analysis-Based Robust Hyperspectral Image Supervised Classification Factors Impacting the Label Denoising of Neural Relation Extraction Classification with imperfect labels for fault prediction Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor Semi-supervised Fuzzy c-Means Variants: A Study on Noisy Label Supervision Get another label? improving data quality and data mining using multiple, noisy labelers Deep Speech: Scaling up end-to-end speech recognition Simple and Effective Multi-Paragraph Reading Comprehension
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