We develop discriminative training algorithms to jointly optimize these entire models end-to-end . First , we proposed a unified model which combines a deep neural network with a latent topic model for image classification . The hybrid model is shown to outperform models based solely on neural networks or topic model alone . In the last couple of years , several adversarial attack methods based on different threat models have been proposed for the image classification problem . Most existing defenses consider additive threat models in which sample perturbations have bounded L_p norms . Many real-world problems exhibit the coexistence of multiple types of heterogeneity , such as view heterogeneity ( i.e. , multi-view property ) and task heterogeneity ( i.e. , multi-task property ) . For example , in an image classification problem containing multiple poses of the same object , each pose can be considered as one view , and the detection of each type of object can be treated as one task . Furthermore , in some problems , the data type of multiple views might be different . In this paper , we propose a multi-manifold deep metric learning ( MMDML ) method for image set classification , which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations . Motivated by the fact that manifold can be effectively used to model the nonlinearity of samples in each image set and deep learning has demonstrated superb capability to model the nonlinearity of samples , we propose a MMDML method to learn multiple sets of nonlinear transformations , one set for each object class , to nonlinearly map multiple sets of image instances into a shared feature subspace , under which the manifold margin of different class is maximized , so that both discriminative and class-specific information can be exploited , simultaneously . The framework makes use of computationally efficient 3D implementations of short local image descriptors , such as LBP , BRIEF and HOG , which are processed by a Bag-of-Patterns model to represent image regions , as well as deep-learned features acquired by denoising auto-encoders and hand-crafted shape features calculated on segmentation masks . The framework is validated against a cohort of 116 brain tumor patients from the TCIA database and is shown to obtain high accuracies even though the same image-based classification task is hardly possible for medical experts . In this paper , we discuss an application of SVM ensemble classifiers to the problem , and explore a confidence indicator based late fusion method to resolve ambiguity across competing classes . Using a matrix of closeness and a set of additional fusion rules , the proposed method improves the classification performance by only subjecting likely misclassified samples to a text-based classifier followed by additional fusion of both image-based classification and text-based classification results . An empirical evaluation using standard ImageClef2010 Medical Retrieval data show very promising performance for the proposed approach . Also , in the evaluation of the accuracy of the training data and the evaluation data , a study was conducted to prevent the overfitting by reducing the difference between the two values . At the same time , we study the parameter setting and model instantiation for improvement of image data classification by comparing image classification through convolution neural network and accuracy and loss rate in image classification through classifiers such as AdaBoostClassifier and RandomForestClassifier . Multidimensional image filtering is adopted for the final data extraction . The image data classification performance is compared with microarray data . Results suggest the most effective means of breast cancer identification in the early stage is a hybrid approach . This paper presents the findings on Object Recognition using Bag-of-Words model done by the author . For the past decades , object recognition had been used in image processing of many fields to carry out tasks such as image classifying , video searching , robot localization and optical character recognition ( OCR ) . Among all these object recognition method , Bag-of-Words ( BOWs ) or Bag-of-Features ( BOFs ) is a very popular approaches because of its simplicity in object recognition . We used convolutional neural network to classify food images . The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying , object detection and other computer vision problems . We classified a food dataset consisting different food categories with 16643 images . The training dataset consisted of 259 condyles , 105 from control subjects and 154 from patients with diagnosis of TMJ OA . For the image analysis classification , 34 right and left condyles from 17 patients ( 39.9 ± 11.7 years ) , who experienced signs and symptoms of the disease for less than 5 years , were included as the testing dataset . For the integrative statistical model of clinical , biological and imaging markers , the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects ( 39.4 ± 15.4 years ) , who did not show any sign or symptom of OA . In this paper , the objective is image classification analysis based on the well known image descriptors , the Scale Invariant Feature Transform ( SIFT ) and the Speeded up Robust Features ( SURF ) on five online available standard datasets . For the classification framework , we adopted the visual words approach . Different from those works , our work aims to solve the semantic segmentation task , which is more challenging due to the large variance in pixel - level features . Domain Adaptation for Semantic Segmentation : So far most works in domain adaptation focus on the task of image classification . Not until recently has the community pay attention to domain shift problem in semantic segmentation . section : Introduction The successes of deep convolutional neural nets ( CNNs ) at image classification has spawned a flurry of work in computer vision on adapting these models to pixel - level image understanding tasks , such as boundary detection , semantic segmentation , optical flow , and pose estimation . The key ideas that have enabled this adaption thus far are : ( 1 ) deconvolution schemes that allow for upsampling coarse pooled feature maps to make detailed predictions at the spatial resolution of individual pixels , ( 2 ) skip connections and hyper - columns which concatenate representations across multi - resolution feature maps , ( 3 ) atrous convolution which allows efficient computation with large receptive fields while maintaining spatial resolution , and ( 4 ) fully convolutional operation which handles variable sized input images . section : Introduction The image classification problem has been redefined by the emergence of large scale datasets such as ImageNet . Since deep learning methods dominated recent Large - Scale Visual Recognition Challenges ( ILSVRC12 - 14 ) , the attention of the computer vision community has been drawn to Convolutional Neural Networks ( CNN ) . Abstract : Efforts were directed toward the following tasks : Survey and organize a list of analytic feature evaluation functions for use in information acquisition tasks ; Formulate military situation assessment tasks as hierarchical multiperspective pattern classification problems , and develop an approach for a decision support system for situation assessment ; Design , develop , and test the Information Acquisition software by which computer-based simulation of behavioral and analytical information acquisition strategies may be investigated ; Design , develop and test the INFOACQ . EXP software which tracks information acquisition strategies employed by human decision maker in sequential classification tasks . The paper presents a concept of bio-prosthesis control via recognition of user intent on the basis of EMG signal analysis . The contextual ( sequential ) recognition is considered and the following methods of sequential classification are explored : Bayes approach with Markov model , multilayer perceptron , multiclassifier with competence function , classifier based on fuzzy logic and classifier based on Dempster-Shafer theory of evidence . The concept of measurement stand which was the source of information exploited in the experimental investigations of algorithms is described . In this paper , we present a method that combines the merits of Bayesian nonparametrics , specifically stick-breaking priors , and large-margin kernel machines in the context of sequential data classification . The proposed model employs a set of ( theoretically ) infinite interdependent large-margin classifiers as model components , that robustly capture local nonlinearity of complex data . The proposed method performs well on an ensemble of medium-sized sparse classification problems . It offers an alternative to global sparsity approaches , and is a natural framework for sequential classification problems . The method extends easily to a whole family of sparsity-related problem which would otherwise require developing specific solutions . Because the emission pdfs enhanced by kernel methods have sufficient nonlinear classification performance , mixture models such as Gaussian mixture models ( GMMs ) , which might cause problems of overfitting and local optima , are not necessary in the proposed method . Unlike the methods used in earlier studies on sequential pattern classification using kernel methods , our method can be regarded as an extension of conventional HMMs , and therefore , it can completely model the transition of hidden states with the observed vectors . Therefore , our method can be applied to many applications developed with conventional HMMs , especially for speech recognition . They can hardly be identified or further processed from a statistical perspective . In this paper , we introduce recurrent neural networks ( RNNs ) to mine and exploit long-term temporal patterns in streams and solve problems of sequential pattern classification , denoising , and deinterleaving of pulse streams . RNNs mine temporal patterns from previously collected streams of certain classes via supervised learning . We show how these changes are related to and improve on alternative recently proposed gating mechanisms such as chrono-initialization and Ordered Neurons . Empirically , our simple gating mechanisms robustly improve the performance of recurrent models on a range of applications , including synthetic memorization tasks , sequential image classification , language modeling , and reinforcement learning , particularly when long-term dependencies are involved . The purpose of this paper is to derive optimal rules for sequential classification problems . In a sequential classification test , for instance , in an educational context , the decision is to classify a student as a master , a partial master , a nonmaster , or continue testing and administering another random item . The framework of minimax sequential decision theory is used by minimizing the maximum expected losses associated with all possible decision rules at each stage of testing . We performed experiments on real protein sequence datasets to test the effectiveness and efficiency of our method . A case study of sequential data set classification was carried out and high accuracy was obtained . The results show that our method is able to be effectively used in the classification of sequential datasets . This paper introduced a divide-and-conquer speech recognition error correction scheme . Firstly transformed continuous speech recognition problem into sequential , independent , classification tasks using confusion network ( CN ) . Each of these sub-tasks could be taken as an isolated word recognition problem and specialized support vector machines ( SVMs ) were trained and applied to each problem to discriminate the recognized candidates from CN . We can also extend the QRNN architecture using techniques introduced for convolutional networks . For sequence classification tasks , we found it helpful to use skip - connections between every QRNN layer , a technique termed “ dense convolution ” by . Where traditional feed - forward or convolutional networks have connections only between subsequent layers , a “ DenseNet ” with layers has feed - forward or convolutional connections between every pair of layers , for a total of . subsection : Pixel - by - pixel MNIST Sequential classification on the MNIST digits is commonly used to test the performance of RNNs . We first implement two settings . We investigate the utility of tabu search ( TS ) meta-heuristics for semi-supervised image classification tasks . The proposed heuristic solves the integer programming transductive support vector machine ( MIP-TSVM ) formulation considered . Other methods compared are support vector machines ( SVMs ) , unsupervised clustering , and non-parametric statistical tests . While not specific to vision tasks , the mi-NB algorithm may find uses in semi-supervised image categorization tasks . This paper presents a novel framework that can combine latent semantic learning and reduced hypergraph learning for semi-supervised image categorization . To improve the traditional bag-of-features representation , we first propose a semantics-aware representation which can learn latent semantics automatically from a large vocabulary of abundant visual keywords through contextual spectral embedding . This work tackles the problem of semi-supervised learning of image classifiers . Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning . This work tackles the problem of semi-supervised learning of image classifiers . Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning . The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform unsupervised clustering or semi-supervised classification of images . Combining the power of these two generative models , we introduce a novel network architecture , Multi-Adversarial Variational autoEncoder Networks ( MAVENs ) , which incorporate an ensemble of discriminators in a combined VAE-GAN network , with simultaneous adversarial learning and variational inference . Semi-supervised image classification is widely applied in various pattern recognition tasks . Label propagation , which is a graph-based semi-supervised learning method , is very popular in solving the semi-supervised image classification problem . The most important step in label propagation is graph construction . In addition , we verified that a technique of gradually increasing of perturbation region further improve the robustness . In experiments , we show that RAT significantly improves classification performance on CIFAR-10 and SVHN compared to existing regularization methods under standard semi-supervised image classification settings . Semi-supervised IMSAT can amplify specific differences and avoid undesirable misclassification in accordance with the purpose . We demonstrate that semi-supervised IMSAT has a comparable performance with existing methods for semi-supervised learning of image classification and can also classify real experimental data ( X-ray diffraction patterns and thermoelectric hysteresis curves ) in the same way even though their shape and dimensions are different . Our algorithm will contribute to the automation of big data processing and artificial intelligence-driven material development . References . 10 Mean kernels for semi-supervised remote sensing image classification ( Luis Gomez-Chova , Javier Calpe-Maravilla , Lorenzo Bruzzone and Gustavo Camps-Valls ) . Here we demonstrate a new deep generative model for classification in this regime . Our model , a Gaussian mixture deep generative model , demonstrates superior semi-unsupervised classification performance on MNIST to model M2 from Kingma and Welling ( 2014 ) . We apply the model to human accelerometer data , performing activity classification and structure discovery on windows of time series data . In this work , we challenge the long-standing success of consistency regularization by introducing self-supervised regularization as the basis for combining semantic feature representations from unlabeled data . We perform extensive comparative experiments to demonstrate the effectiveness of self-supervised regularization for supervised and semi-supervised image classification on SVHN , CIFAR-10 , and CIFAR-100 benchmark datasets . We present two main results : ( 1 ) models augmented with self-supervised regularization significantly improve upon traditional supervised classifiers without the need for unlabeled data ; ( 2 ) together with unlabeled data , our models yield semi-supervised performance competitive with , and in many cases exceeding , prior state-of-the-art consistency baselines . Though the reparameterisation trick for continuous variables is not applicable for this case , a policy gradient approach mnih2014neural can help to alleviate the high variance problem during stochastic estimation . proposed a variational inference framework for semi - supervised learning , but the prior distribution over the hidden variable remains as the standard Gaussian prior , while we apply a conditional parameterised Gaussian distribution , which is jointly learned with the variational distribution . section : Neural Variational Document Model [ reference ] ) . This setup allows us to do semi - supervised learning , where the biLM is pretrained at a large scale ( Sec . [ reference ] ) and easily incorporated into a wide range of existing neural NLP architectures ( Sec . Most of these approaches can not scale to large graphs , which is improved by using a localized first - order approximation of spectral graph convolution kipf2016semi and further equipping with important sampling for deriving a fast GCN chen2018fastgcn . The closely relevant work to our graph encoder is GCN kipf2016semi , which is designed for semi - supervised learning in transductive setting that requires full graph Laplacian to be given during training and is typically applicable to a single large undirected graph . An extension of GCN can be shown to be mathematically related to one variant of our graph encoder on undirected graphs . ( unsupervised ) with an accuracy of 95 % , and Zhang et al . ( semi - supervised , 20 labels ) with an accuracy of 96 % . Fig . We believe this happens because we back - propagate the target loss through both networks ( in contrast to - model ) that leads to weights getting updated using target - based gradients more often . Even though we designed our algorithm specifically to address problems in RNNs , to have a fair comparison , we compare with - model on a semi - supervised task which is their goal . Specifically , we use the CIFAR - 10 dataset that consists of images from 10 classes . Following the usual splits used in semi - supervised learning literature , we use 4 thousand labeled and 41 thousand unlabeled samples for training , 5 thousand labeled samples for validation and 10 thousand labeled samples for test set . We use the original ResNet - 56 architecture . This observation inspired us to resort to the GAN samples to enlarge and enrich the training set . It also motivated us to employ the proposed regularization to implement a semi - supervised system . In an attempt to overcome the above - mentioned challenges , this paper 1 ) adopts GAN in unlabeled data generation , 2 ) proposes We show how the adversarial autoencoder can be used in applications such as semi - supervised classification , disentangling style and content of images , unsupervised clustering , dimensionality reduction and data visualization . We performed experiments on MNIST , Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi - supervised classification tasks . section : Introduction As shown in the first column of le : semi ] Table [ reference ] , the performance of PixelGAN autoencoders is on par with other GAN - based clustering algorithms such as CatGAN catgan , InfoGAN infogan and adversarial autoencoders aae . subsection : Semi - supervised Classification le : semi ] Table [ reference ] and : plot ] Figure [ reference ] report the results of semi - supervised classification experiments on the MNIST , SVHN and NORB datasets . document : Semi - Supervised Classification with Graph Convolutional Networks We present a scalable approach for semi - supervised learning on graph - structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs . The softmax activation function , defined as with , is applied row - wise . For semi - supervised multi - class classification , we then evaluate the cross - entropy error over all labeled examples : where is the set of node indices that have labels . The neural network weights and are trained using gradient descent . Here , is the balanced binary cross - entropy loss with weighting factor , is the sigmoid function , is the Hadamard ( element - wise ) product , and is the Boolean mask : if , else . Semi - Supervised Node Classification The MTGAE model can also be used to perform efficient information propagation on graphs for the task of semi - supervised node classification . For more theoretical overview of related methods of noise regularizations , Jacobian regularizations and their extensions like PEA [ reference ] , ladder networks [ reference ] and VAT , we refer the readers to [ reference ] . Several works [ reference ] , [ reference ] succeeded in using the random image augmentation to improve generalization performance for semi - supervised tasks of image classification . These methods can also be interpreted as types of techniques that smooth the model around input data points and extrapolate the labels of unlabeled examples . Combination of these two ideas is a future work . section : APPENDIX A SUPERVISED CLASSIFICATION FOR THE MNIST DATASET The MNIST dataset consists of 28 × 28 pixel images of handwritten digits and their corresponding labels . where φ i is used to denote the parameters updated by gradient descent and where the loss corresponds to negative log likelihood of the data . In particular , in the case of supervised classification with inputs { x j } , their corresponding labels { y j } , and a classifier f θ , we will denote the negative log likelihood of the data under the classifier as L ( θ , D ) = − ( xj , yj ) ∈D log To the best of our knowledge , no standard parsers have ever been trained on datasets numbering in the hundreds of millions of tokens , and it would be hard to do due to efficiency problems . We therefore cite the semi - supervised results , which are analogous in spirit but use less data . Table [ reference ] shows performance of our models on the top and results from other papers at the bottom . Than Audio ? The Effect of Joint Use of Tags and Audio Content Features for Artistic Style Clustering . Social tags are receiving growing interests in information retrieval . In music information retrieval previous research has demonstrated that tags can assist in music classification and clustering . This paper studies the problem of combining tags and audio contents for artistic style clustering . After studying the effectiveness of using tags and audio contents separately for clustering , this paper proposes a novel language model that makes use of both data sources . This paper describes an approach to automatically classify digital pictures of paintings by artistic genre . While the task of artistic classification is often entrusted to human experts , recent advances in machine learning and multimedia feature extraction has made this task easier to automate . Automatic classification is useful for organizing large digital collections , for automatic artistic recommendation , and even for mobile capture and identification by consumers . In addition , we propose a linear propagation module to enable a feed-forward network for photo-realistic style transfer . We demonstrate the effectiveness of our approach on three tasks : artistic style , photo-realistic and video style transfer , with comparisons to state-of-the-art methods . The goal for the whole is to transmit the Chinese painting style to three-dimension models . In this system , the input artwork is first segmented into several regions used for modeling and artistic style analysis : 1 ) region contours and skeletons is found to build three-dimension models by surface inflation technique , 2 ) art style attributes from regions , such as color , texture and stroke width , are analyzed to maintain the artistic style coherence . In addition , surface features on 3D models are extracted to guide the process of Ink-style rendering . Given a sketch , with no semantic tagging , and a reference image of a specific style , the model can synthesize meaningful details with colors and textures . The model consists of three modules designed explicitly for better artistic style capturing and generation . Based on a GAN framework , a dual-masked mechanism is introduced to enforce the content constraints ( from the sketch ) , and a feature-map transformation technique is developed to strengthen the style consistency ( to the reference image ) . Computerized classification of traditional Chinese ink-wash paintings via artistic style analysis . Artistic style description via extraction of global and local features . A windowed and entropy balanced fusion scheme to integrate the classification results . We propose a novel method for detecting the artistic style of a painting that is motivated by the neural-style algorithm of Gatys et . al. and is competitive with other recent algorithmic approaches to artistic style detection . We annotate Behance imagery with rich attribute labels for content , emotions , and artistic media . Furthermore , we carry out baseline experiments to show the value of this dataset for artistic style prediction , for improving the generality of existing object classifiers , and for the study of visual domain adaptation . We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems . Visual characteristics of paintings display high-level semantic concept : art style to the viewers . Classification of art style depends mainly on human knowledge and experience , which remains a big challenge for computer vision . In this paper , based on careful studies on art literature , we propose a simple but effective method to automatically identify the art style between the Chinese wash painting and the foreign art painting . The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists , a problem that was not addressed before in a general setting . We first present a comparative study of different classification methodologies for the task of fine-art style classification . A two-level comparative study is performed for this classification problem .
We develop discriminative training algorithms to jointly optimize these entire models end-to-end . First , we proposed a unified model which combines a deep neural network with a latent topic model for image classification . The hybrid model is shown to outperform models based solely on neural networks or topic model alone .
In the last couple of years , several adversarial attack methods based on different threat models have been proposed for the image classification problem . Most existing defenses consider additive threat models in which sample perturbations have bounded L_p norms .
Many real-world problems exhibit the coexistence of multiple types of heterogeneity , such as view heterogeneity ( i.e. , multi-view property ) and task heterogeneity ( i.e. , multi-task property ) . For example , in an image classification problem containing multiple poses of the same object , each pose can be considered as one view , and the detection of each type of object can be treated as one task . Furthermore , in some problems , the data type of multiple views might be different .
In this paper , we propose a multi-manifold deep metric learning ( MMDML ) method for image set classification , which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations . Motivated by the fact that manifold can be effectively used to model the nonlinearity of samples in each image set and deep learning has demonstrated superb capability to model the nonlinearity of samples , we propose a MMDML method to learn multiple sets of nonlinear transformations , one set for each object class , to nonlinearly map multiple sets of image instances into a shared feature subspace , under which the manifold margin of different class is maximized , so that both discriminative and class-specific information can be exploited , simultaneously .
The framework makes use of computationally efficient 3D implementations of short local image descriptors , such as LBP , BRIEF and HOG , which are processed by a Bag-of-Patterns model to represent image regions , as well as deep-learned features acquired by denoising auto-encoders and hand-crafted shape features calculated on segmentation masks . The framework is validated against a cohort of 116 brain tumor patients from the TCIA database and is shown to obtain high accuracies even though the same image-based classification task is hardly possible for medical experts .
In this paper , we discuss an application of SVM ensemble classifiers to the problem , and explore a confidence indicator based late fusion method to resolve ambiguity across competing classes . Using a matrix of closeness and a set of additional fusion rules , the proposed method improves the classification performance by only subjecting likely misclassified samples to a text-based classifier followed by additional fusion of both image-based classification and text-based classification results . An empirical evaluation using standard ImageClef2010 Medical Retrieval data show very promising performance for the proposed approach .
Also , in the evaluation of the accuracy of the training data and the evaluation data , a study was conducted to prevent the overfitting by reducing the difference between the two values . At the same time , we study the parameter setting and model instantiation for improvement of image data classification by comparing image classification through convolution neural network and accuracy and loss rate in image classification through classifiers such as AdaBoostClassifier and RandomForestClassifier .
Multidimensional image filtering is adopted for the final data extraction . The image data classification performance is compared with microarray data . Results suggest the most effective means of breast cancer identification in the early stage is a hybrid approach .
This paper presents the findings on Object Recognition using Bag-of-Words model done by the author . For the past decades , object recognition had been used in image processing of many fields to carry out tasks such as image classifying , video searching , robot localization and optical character recognition ( OCR ) . Among all these object recognition method , Bag-of-Words ( BOWs ) or Bag-of-Features ( BOFs ) is a very popular approaches because of its simplicity in object recognition .
We used convolutional neural network to classify food images . The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying , object detection and other computer vision problems . We classified a food dataset consisting different food categories with 16643 images .
The training dataset consisted of 259 condyles , 105 from control subjects and 154 from patients with diagnosis of TMJ OA . For the image analysis classification , 34 right and left condyles from 17 patients ( 39.9 ± 11.7 years ) , who experienced signs and symptoms of the disease for less than 5 years , were included as the testing dataset . For the integrative statistical model of clinical , biological and imaging markers , the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects ( 39.4 ± 15.4 years ) , who did not show any sign or symptom of OA .
In this paper , the objective is image classification analysis based on the well known image descriptors , the Scale Invariant Feature Transform ( SIFT ) and the Speeded up Robust Features ( SURF ) on five online available standard datasets . For the classification framework , we adopted the visual words approach .
Different from those works , our work aims to solve the semantic segmentation task , which is more challenging due to the large variance in pixel - level features . Domain Adaptation for Semantic Segmentation : So far most works in domain adaptation focus on the task of image classification . Not until recently has the community pay attention to domain shift problem in semantic segmentation .
section : Introduction The successes of deep convolutional neural nets ( CNNs ) at image classification has spawned a flurry of work in computer vision on adapting these models to pixel - level image understanding tasks , such as boundary detection , semantic segmentation , optical flow , and pose estimation . The key ideas that have enabled this adaption thus far are : ( 1 ) deconvolution schemes that allow for upsampling coarse pooled feature maps to make detailed predictions at the spatial resolution of individual pixels , ( 2 ) skip connections and hyper - columns which concatenate representations across multi - resolution feature maps , ( 3 ) atrous convolution which allows efficient computation with large receptive fields while maintaining spatial resolution , and ( 4 ) fully convolutional operation which handles variable sized input images .
section : Introduction The image classification problem has been redefined by the emergence of large scale datasets such as ImageNet . Since deep learning methods dominated recent Large - Scale Visual Recognition Challenges ( ILSVRC12 - 14 ) , the attention of the computer vision community has been drawn to Convolutional Neural Networks ( CNN ) .
Abstract : Efforts were directed toward the following tasks : Survey and organize a list of analytic feature evaluation functions for use in information acquisition tasks ; Formulate military situation assessment tasks as hierarchical multiperspective pattern classification problems , and develop an approach for a decision support system for situation assessment ; Design , develop , and test the Information Acquisition software by which computer-based simulation of behavioral and analytical information acquisition strategies may be investigated ; Design , develop and test the INFOACQ . EXP software which tracks information acquisition strategies employed by human decision maker in sequential classification tasks .
The paper presents a concept of bio-prosthesis control via recognition of user intent on the basis of EMG signal analysis . The contextual ( sequential ) recognition is considered and the following methods of sequential classification are explored : Bayes approach with Markov model , multilayer perceptron , multiclassifier with competence function , classifier based on fuzzy logic and classifier based on Dempster-Shafer theory of evidence . The concept of measurement stand which was the source of information exploited in the experimental investigations of algorithms is described .
In this paper , we present a method that combines the merits of Bayesian nonparametrics , specifically stick-breaking priors , and large-margin kernel machines in the context of sequential data classification . The proposed model employs a set of ( theoretically ) infinite interdependent large-margin classifiers as model components , that robustly capture local nonlinearity of complex data .
The proposed method performs well on an ensemble of medium-sized sparse classification problems . It offers an alternative to global sparsity approaches , and is a natural framework for sequential classification problems . The method extends easily to a whole family of sparsity-related problem which would otherwise require developing specific solutions .
Because the emission pdfs enhanced by kernel methods have sufficient nonlinear classification performance , mixture models such as Gaussian mixture models ( GMMs ) , which might cause problems of overfitting and local optima , are not necessary in the proposed method . Unlike the methods used in earlier studies on sequential pattern classification using kernel methods , our method can be regarded as an extension of conventional HMMs , and therefore , it can completely model the transition of hidden states with the observed vectors . Therefore , our method can be applied to many applications developed with conventional HMMs , especially for speech recognition .
They can hardly be identified or further processed from a statistical perspective . In this paper , we introduce recurrent neural networks ( RNNs ) to mine and exploit long-term temporal patterns in streams and solve problems of sequential pattern classification , denoising , and deinterleaving of pulse streams . RNNs mine temporal patterns from previously collected streams of certain classes via supervised learning .
We show how these changes are related to and improve on alternative recently proposed gating mechanisms such as chrono-initialization and Ordered Neurons . Empirically , our simple gating mechanisms robustly improve the performance of recurrent models on a range of applications , including synthetic memorization tasks , sequential image classification , language modeling , and reinforcement learning , particularly when long-term dependencies are involved .
The purpose of this paper is to derive optimal rules for sequential classification problems . In a sequential classification test , for instance , in an educational context , the decision is to classify a student as a master , a partial master , a nonmaster , or continue testing and administering another random item . The framework of minimax sequential decision theory is used by minimizing the maximum expected losses associated with all possible decision rules at each stage of testing .
We performed experiments on real protein sequence datasets to test the effectiveness and efficiency of our method . A case study of sequential data set classification was carried out and high accuracy was obtained . The results show that our method is able to be effectively used in the classification of sequential datasets .
This paper introduced a divide-and-conquer speech recognition error correction scheme . Firstly transformed continuous speech recognition problem into sequential , independent , classification tasks using confusion network ( CN ) . Each of these sub-tasks could be taken as an isolated word recognition problem and specialized support vector machines ( SVMs ) were trained and applied to each problem to discriminate the recognized candidates from CN .
We can also extend the QRNN architecture using techniques introduced for convolutional networks . For sequence classification tasks , we found it helpful to use skip - connections between every QRNN layer , a technique termed “ dense convolution ” by . Where traditional feed - forward or convolutional networks have connections only between subsequent layers , a “ DenseNet ” with layers has feed - forward or convolutional connections between every pair of layers , for a total of .
subsection : Pixel - by - pixel MNIST Sequential classification on the MNIST digits is commonly used to test the performance of RNNs . We first implement two settings .
We investigate the utility of tabu search ( TS ) meta-heuristics for semi-supervised image classification tasks . The proposed heuristic solves the integer programming transductive support vector machine ( MIP-TSVM ) formulation considered .
Other methods compared are support vector machines ( SVMs ) , unsupervised clustering , and non-parametric statistical tests . While not specific to vision tasks , the mi-NB algorithm may find uses in semi-supervised image categorization tasks .
This paper presents a novel framework that can combine latent semantic learning and reduced hypergraph learning for semi-supervised image categorization . To improve the traditional bag-of-features representation , we first propose a semantics-aware representation which can learn latent semantics automatically from a large vocabulary of abundant visual keywords through contextual spectral embedding .
This work tackles the problem of semi-supervised learning of image classifiers . Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning .
This work tackles the problem of semi-supervised learning of image classifiers . Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning .
The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform unsupervised clustering or semi-supervised classification of images . Combining the power of these two generative models , we introduce a novel network architecture , Multi-Adversarial Variational autoEncoder Networks ( MAVENs ) , which incorporate an ensemble of discriminators in a combined VAE-GAN network , with simultaneous adversarial learning and variational inference .
Semi-supervised image classification is widely applied in various pattern recognition tasks . Label propagation , which is a graph-based semi-supervised learning method , is very popular in solving the semi-supervised image classification problem . The most important step in label propagation is graph construction .
In addition , we verified that a technique of gradually increasing of perturbation region further improve the robustness . In experiments , we show that RAT significantly improves classification performance on CIFAR-10 and SVHN compared to existing regularization methods under standard semi-supervised image classification settings .
Semi-supervised IMSAT can amplify specific differences and avoid undesirable misclassification in accordance with the purpose . We demonstrate that semi-supervised IMSAT has a comparable performance with existing methods for semi-supervised learning of image classification and can also classify real experimental data ( X-ray diffraction patterns and thermoelectric hysteresis curves ) in the same way even though their shape and dimensions are different . Our algorithm will contribute to the automation of big data processing and artificial intelligence-driven material development .
References . 10 Mean kernels for semi-supervised remote sensing image classification ( Luis Gomez-Chova , Javier Calpe-Maravilla , Lorenzo Bruzzone and Gustavo Camps-Valls ) .
Here we demonstrate a new deep generative model for classification in this regime . Our model , a Gaussian mixture deep generative model , demonstrates superior semi-unsupervised classification performance on MNIST to model M2 from Kingma and Welling ( 2014 ) . We apply the model to human accelerometer data , performing activity classification and structure discovery on windows of time series data .
In this work , we challenge the long-standing success of consistency regularization by introducing self-supervised regularization as the basis for combining semantic feature representations from unlabeled data . We perform extensive comparative experiments to demonstrate the effectiveness of self-supervised regularization for supervised and semi-supervised image classification on SVHN , CIFAR-10 , and CIFAR-100 benchmark datasets . We present two main results : ( 1 ) models augmented with self-supervised regularization significantly improve upon traditional supervised classifiers without the need for unlabeled data ; ( 2 ) together with unlabeled data , our models yield semi-supervised performance competitive with , and in many cases exceeding , prior state-of-the-art consistency baselines .
Though the reparameterisation trick for continuous variables is not applicable for this case , a policy gradient approach mnih2014neural can help to alleviate the high variance problem during stochastic estimation . proposed a variational inference framework for semi - supervised learning , but the prior distribution over the hidden variable remains as the standard Gaussian prior , while we apply a conditional parameterised Gaussian distribution , which is jointly learned with the variational distribution . section : Neural Variational Document Model
[ reference ] ) . This setup allows us to do semi - supervised learning , where the biLM is pretrained at a large scale ( Sec . [ reference ] ) and easily incorporated into a wide range of existing neural NLP architectures ( Sec .
Most of these approaches can not scale to large graphs , which is improved by using a localized first - order approximation of spectral graph convolution kipf2016semi and further equipping with important sampling for deriving a fast GCN chen2018fastgcn . The closely relevant work to our graph encoder is GCN kipf2016semi , which is designed for semi - supervised learning in transductive setting that requires full graph Laplacian to be given during training and is typically applicable to a single large undirected graph . An extension of GCN can be shown to be mathematically related to one variant of our graph encoder on undirected graphs .
( unsupervised ) with an accuracy of 95 % , and Zhang et al . ( semi - supervised , 20 labels ) with an accuracy of 96 % . Fig .
We believe this happens because we back - propagate the target loss through both networks ( in contrast to - model ) that leads to weights getting updated using target - based gradients more often . Even though we designed our algorithm specifically to address problems in RNNs , to have a fair comparison , we compare with - model on a semi - supervised task which is their goal . Specifically , we use the CIFAR - 10 dataset that consists of images from 10 classes . Following the usual splits used in semi - supervised learning literature , we use 4 thousand labeled and 41 thousand unlabeled samples for training , 5 thousand labeled samples for validation and 10 thousand labeled samples for test set . We use the original ResNet - 56 architecture .
This observation inspired us to resort to the GAN samples to enlarge and enrich the training set . It also motivated us to employ the proposed regularization to implement a semi - supervised system . In an attempt to overcome the above - mentioned challenges , this paper 1 ) adopts GAN in unlabeled data generation , 2 ) proposes
We show how the adversarial autoencoder can be used in applications such as semi - supervised classification , disentangling style and content of images , unsupervised clustering , dimensionality reduction and data visualization . We performed experiments on MNIST , Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi - supervised classification tasks . section : Introduction
As shown in the first column of le : semi ] Table [ reference ] , the performance of PixelGAN autoencoders is on par with other GAN - based clustering algorithms such as CatGAN catgan , InfoGAN infogan and adversarial autoencoders aae . subsection : Semi - supervised Classification le : semi ] Table [ reference ] and : plot ] Figure [ reference ] report the results of semi - supervised classification experiments on the MNIST , SVHN and NORB datasets .
document : Semi - Supervised Classification with Graph Convolutional Networks We present a scalable approach for semi - supervised learning on graph - structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs .
The softmax activation function , defined as with , is applied row - wise . For semi - supervised multi - class classification , we then evaluate the cross - entropy error over all labeled examples : where is the set of node indices that have labels . The neural network weights and are trained using gradient descent .
Here , is the balanced binary cross - entropy loss with weighting factor , is the sigmoid function , is the Hadamard ( element - wise ) product , and is the Boolean mask : if , else . Semi - Supervised Node Classification The MTGAE model can also be used to perform efficient information propagation on graphs for the task of semi - supervised node classification .
For more theoretical overview of related methods of noise regularizations , Jacobian regularizations and their extensions like PEA [ reference ] , ladder networks [ reference ] and VAT , we refer the readers to [ reference ] . Several works [ reference ] , [ reference ] succeeded in using the random image augmentation to improve generalization performance for semi - supervised tasks of image classification . These methods can also be interpreted as types of techniques that smooth the model around input data points and extrapolate the labels of unlabeled examples .
Combination of these two ideas is a future work . section : APPENDIX A SUPERVISED CLASSIFICATION FOR THE MNIST DATASET The MNIST dataset consists of 28 × 28 pixel images of handwritten digits and their corresponding labels .
where φ i is used to denote the parameters updated by gradient descent and where the loss corresponds to negative log likelihood of the data . In particular , in the case of supervised classification with inputs { x j } , their corresponding labels { y j } , and a classifier f θ , we will denote the negative log likelihood of the data under the classifier as L ( θ , D ) = − ( xj , yj ) ∈D log
To the best of our knowledge , no standard parsers have ever been trained on datasets numbering in the hundreds of millions of tokens , and it would be hard to do due to efficiency problems . We therefore cite the semi - supervised results , which are analogous in spirit but use less data . Table [ reference ] shows performance of our models on the top and results from other papers at the bottom .
Than Audio ? The Effect of Joint Use of Tags and Audio Content Features for Artistic Style Clustering . Social tags are receiving growing interests in information retrieval . In music information retrieval previous research has demonstrated that tags can assist in music classification and clustering . This paper studies the problem of combining tags and audio contents for artistic style clustering . After studying the effectiveness of using tags and audio contents separately for clustering , this paper proposes a novel language model that makes use of both data sources .
This paper describes an approach to automatically classify digital pictures of paintings by artistic genre . While the task of artistic classification is often entrusted to human experts , recent advances in machine learning and multimedia feature extraction has made this task easier to automate . Automatic classification is useful for organizing large digital collections , for automatic artistic recommendation , and even for mobile capture and identification by consumers .
In addition , we propose a linear propagation module to enable a feed-forward network for photo-realistic style transfer . We demonstrate the effectiveness of our approach on three tasks : artistic style , photo-realistic and video style transfer , with comparisons to state-of-the-art methods .
The goal for the whole is to transmit the Chinese painting style to three-dimension models . In this system , the input artwork is first segmented into several regions used for modeling and artistic style analysis : 1 ) region contours and skeletons is found to build three-dimension models by surface inflation technique , 2 ) art style attributes from regions , such as color , texture and stroke width , are analyzed to maintain the artistic style coherence . In addition , surface features on 3D models are extracted to guide the process of Ink-style rendering .
Given a sketch , with no semantic tagging , and a reference image of a specific style , the model can synthesize meaningful details with colors and textures . The model consists of three modules designed explicitly for better artistic style capturing and generation . Based on a GAN framework , a dual-masked mechanism is introduced to enforce the content constraints ( from the sketch ) , and a feature-map transformation technique is developed to strengthen the style consistency ( to the reference image ) .
Computerized classification of traditional Chinese ink-wash paintings via artistic style analysis . Artistic style description via extraction of global and local features . A windowed and entropy balanced fusion scheme to integrate the classification results .
We propose a novel method for detecting the artistic style of a painting that is motivated by the neural-style algorithm of Gatys et . al. and is competitive with other recent algorithmic approaches to artistic style detection .
We annotate Behance imagery with rich attribute labels for content , emotions , and artistic media . Furthermore , we carry out baseline experiments to show the value of this dataset for artistic style prediction , for improving the generality of existing object classifiers , and for the study of visual domain adaptation . We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems .
Visual characteristics of paintings display high-level semantic concept : art style to the viewers . Classification of art style depends mainly on human knowledge and experience , which remains a big challenge for computer vision . In this paper , based on careful studies on art literature , we propose a simple but effective method to automatically identify the art style between the Chinese wash painting and the foreign art painting .
The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists , a problem that was not addressed before in a general setting . We first present a comparative study of different classification methodologies for the task of fine-art style classification . A two-level comparative study is performed for this classification problem .
Joint Training of a Neural Network and a Structured Model for Computer Vision Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks Deep Multimodality Model for Multi-task Multi-view Learning Multi-manifold deep metric learning for image set classification Multi-modal Image Classification Using Low-Dimensional Texture Features for Genomic Brain Tumor Recognition Image modality classification: a late fusion method based on confidence indicator and closeness matrix Deep Learning based on Object Identification Rate in Video Research for improving image classification Early Breast Cancer Identification: Which Way to Go? Microarray or Image Based Computer Aided Diagnosis! Object Recognition Using Bows Model Image Recognition with Deep Learning A web-based system for neural network based classification in temporomandibular joint osteoarthritis EVALUATION OF SIFT AND SURF USING BAG OF WORDS MODEL ON A LARGE DATASET ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Recurrent Pixel Embedding for Instance Grouping Evaluation of output embeddings for fine-grained image classification Mutual Adaptiveness of Man and Machine in Information Acquisition Tasks Sequential recognition of EMG signals applied to the control of bioprosthetic hand: experimental comparative analysis of methods Infinite Markov-Switching Maximum Entropy Discrimination Machines Sequential approaches for learning datum-wise sparse representations A Sequential Pattern Classifier Based on Hidden Markov Kernel Machine and Its Application to Phoneme Classification Classification, Denoising, and Deinterleaving of Pulse Streams With Recurrent Neural Networks Improving the Gating Mechanism of Recurrent Neural Networks A minimax solution for sequential classification problems. Measuring similarity between sequential datasets Speech recognition error correction scheme based on divide-and-conquer Quasi-Recurrent Neural Networks Dilated Recurrent Neural Networks A Tabu Search Meta-Heuristic for Image Semi-Supervised Classification Visual recognition, inference and coding using learned sparse overcomplete representations Combining latent semantic learning and reduced hypergraph learning for semi-supervised image categorization S$^\mathbf{4}$L: Self-Supervised Semi-Supervised Learning S4L: Self-Supervised Semi-Supervised Learning Multi-adversarial Variational Autoencoder Networks Semi-supervised image classification via nonnegative least-squares regression Adversarial Transformations for Semi-Supervised Learning Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training Kernel methods for remote sensing data analysis Semi-unsupervised Learning of Human Activity using Deep Generative Models Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning Neural Variational Inference for Text Processing Deep contextualized word representations Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Inferencing based on unsupervised learning of disentangled representations Fraternal Dropout Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Adversarial Autoencoders PixelGAN Autoencoders Semi-Supervised Classification with Graph Convolutional Networks Multi-Task Graph Autoencoders Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Probabilistic Model-Agnostic Meta-Learning Grammar as a Foreign Language Are Tags Better Than Audio? The Effect of Joint Use of Tags and Audio Content Features for Artistic Style Clustering Classifying paintings by artistic genre: An analysis of features & classifiers Learning Linear Transformations for Fast Image and Video Style Transfer Artwork-Based 3D Ink Style Modeling and Rendering Sketch-to-Art: Synthesizing Stylized Art Images From Sketches Recognition of Chinese artists via windowed and entropy balanced fusion in classification of their authored ink and wash paintings (IWPs) Towards the Algorithmic Detection of Artistic Style BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography Learning to Recognize the Art Style of Paintings Using Multi-cues Toward automated discovery of artistic influence
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