In this work , we propose the expedited convolutional neural networks involving Tucker decomposition to recognize MCI using MRI images . We employ transfer learning and data augmentation to deal with limited training data . The effect of Tucker decomposition on saving computational time is discussed . Then , we produce a large set of “ multi-class ” artificial samples , by interchanging the periocular and ocular parts from different subjects . These samples are used for data augmentation purposes and feed the learning phase of the CNN , always considering as label the ID of the periocular part . This way , for every periocular region , the CNN receives multiple samples of different ocular classes , forcing it to conclude that such regions should not be considered in its response . However , poor performance of these methods can result from a lack of quantity or diversity in the data used to train the models . Recently , generative adversarial networks ( GANs ) have demonstrated the ability to generate synthetic images , which may be useful for data augmentation purposes . Here , we propose the application of GANs to construct chorio-retinal patches from OCT images which may be used to augment data for a patch-based approach to boundary segmentation . We describe the initial version of VERIFAI which centers on simulation guided by formal models and specifications . Several use cases are illustrated with examples , including temporal-logic falsification , model-based systematic fuzz testing , parameter synthesis , counterexample analysis , and data set augmentation . We introduce a forward simulation MCMC ( fsMCMC ) algorithm , which is primarily based upon simulation from the model . The fsMCMC algorithm formulates the simulation of the process explicitly as a data augmentation problem . By exploiting non-centred parameterizations , an efficient MCMC updating schema for the parameters and augmented data is introduced , whilst maintaining straightforward simulation from the model . The images are modelized by hidden Markov fields with unknown parameters . Given the observed images , we give a Bayesian formulation and we propose to solve the resulting data augmentation problem by implementing a Monte Carlo Markov chain ( MCMC ) procedure . We separate the unknown variables into two categories : ( 1 ) the parameters of interest which are the mixing matrix , the noise covariance and the parameters of the sources distributions ; and ( 2 ) the hidden variables which are the unobserved sources and the unobserved pixels classification labels . This paper presents a novel strategy that employs Generative Adversarial Networks ( GANs ) to augment data in the skin lesion segmentation task , which is a fundamental first step in the automated melanoma detection process . The proposed framework generates both skin lesion images and their segmentation masks , making the data augmentation process extremely straightforward . In order to thoroughly analyze how the quality and diversity of synthetic images impact the efficiency of the method , we remodel two different well known GANs : a Deep Convolutional GAN ( DCGAN ) and a Laplacian GAN ( LAPGAN ) . Therefore , in this paper , we investigate and introduced a paradigm augmenting big data for IoV intelligent system to optimize massive data exploration in the field . The paradigm of big data augmentation is a systematic approach to development raises great expectations and concern to the analytical value of large-scale data that address IoV in the natural progression of intelligent IoV and cloud computing . The intelligent IoV technology is transforming to cloud system to satisfy a variety of IoV applications and user needs , which provide analytic and access of massive data . In this paper , we focus on simple data-driven approach to solve deep learning based on implementing the Mask R-CNN module by analyzing deeper manipulation of datasets . We firstly approach to affine transformation and projective representation to data augmentation analysis in order to increasing large-scale data manually based on the state-of-the-art in views of computer vision . Then we evaluate our method concretely by connection our datasets by visualization data and completely in testing to many methods to understand intelligent data analysis in object detection and segmentation by using more than 5000 image according to many similar objects . In this approach , the use of the 3-Dimensional Convolutional Neural Networks ( 3D-CNN ) is investigated using a multi-channel EEG data for emotion recognition . A data augmentation phase is developed to enhance the performance of the proposed 3D-CNN approach . And , a 3D data representation is formulated from the multi-channel EEG signals , which is used as data input for the proposed 3D-CNN model . This is important because social media platforms are increasingly sharing their data and because of the recent rise of social media based on pictures . Pictorial data may soon become a viable option for data data-augmentation . Instead , we keep the original aspect ratio of images fixed and resize them to five different scales ( setting their maximum of width or height to { 480 , 576 , 688 , 864 , 1200 } respectively ) as in [ reference ] . During training , we apply random horizontal flips to the images and select a scale at random as a form of jittering or data augmentation . At test time we average the outputs of 10 images ( i.e. the 5 scales and their flips ) . The top of the network produces an embedding containing coarse and fine - grained information , so that images can be recognized based on the object class , particular object , or if they are distorted copies . Our joint training is simple : we minimize a cross - entropy loss for classification and a ranking loss that determines if two images are identical up to data augmentation , with no need for additional labels . A key component of MultiGrain is a pooling layer that takes advantage of high - resolution images with a network trained at a lower resolution . appendix : Data augmentation hyper - parameters tab : full - data - augment gives the transformations in the full data augmentation used in our experiments ( sec : experimental - settings ) , along with their parameters . appendix : Additional results and ablation study for Multigrain in retrieval These promising results are presented in this paper to demonstrate the potential benefits of the method ; clearly further innovations within the method are required if it is to be competitive on harder datasets like CIFAR - 10 , or Imagenet . We expect that the most likely avenues for improving our presented results for CIFAR - 10 , whilst retaining the method ’s core attributes , are ( 1 ) to introduce limited training of the Stage 1 filters by generalizing the method of ; ( 2 ) introduction of training data augmentation . We aim to pursuing these directions in our future work . Note that the parameters of the batch normalisation modules are kept unchanged in the whole learning process . Online data - augmentation is performed during training to regularise the model . The training images are randomly mirrored , scaled by a ratio between 0.5 and 2 , rotated by an angle between - 10 and 10 degrees , translated by a random amount in the HSV colour space , and blurred with a randomly - sized Gaussian kernel , all on - the - fly . In the following experiments , all models are trained using Adam , with a learning rate of 1e - 4 on a NVidia Titan X GPU . Leaf segmentation Since this dataset only consists of 128 images , we use online data augmentation to prevent the model from overfitting and to increase the overall robustness . We apply random left - right flip , random rotation with and random scale deformation with . This is not specific to our approach , but a general problem in medical image analysis . One possible way of making the CNN invariant to the data heterogeneity is to learn a generative model for the data acquisition process , and use this model in the data augmentation step . This is a direction we explore as part of future work . The representation used in is not invariant to graph isomorphism . Instead , this invariance must be learned by the downstream model through dataset augmentation . In addition to the eight MPNNâs discussed in Section [ reference ] there have been a number of other approaches to machine learning on graphical data which take advantage of the symmetries in a number of ways . Among them , seq2seq models yuan2016grammatical , xie2016neural , ji2017nested , sakaguchi2017grammatical , schmaltz - EtAl:2017:EMNLP2017 , chollampatt2018 , junczys2018approaching have caught much attention . Unlike the models trained only with original error - corrected data , we propose a novel fluency boost learning mechanism for dynamic data augmentation along with training for GEC , despite some related studies that explore artificial error generation for GEC brockett2006correcting , foster2009generrate , rozovskaya2010training , rozovskaya2011algorithm , Rozovskaya2012The , felice - yuan:2014:SRW , xie2016neural , rei2017artificial , xie2018noising . Moreover , we propose fluency boost inference which allows the model to repeatedly edit a sentence as long as the sentence ’s fluency can be improved . For regression problems at the leaf - nodes , we propose a generalized weighted ridge regression ( GWRR ) as an extension of the work in [ reference ] . GWRR models are generated based on the data distributions from the leaf - nodes . The main contribution of our method is on feature augmentation , so we call our method featureaugmented random forest ( FARF ) . The pipeline of our FARF method , which includes feature extraction , the training stage , and inference stages for SISR , is shown in Fig . 1 . The main goal is to construct a full chain composed of 4 blocks that correspond to the main steps of augmented reality process : feature detection , feature extraction , feature matching and image registration . Our work proposes an improved technique for image augmentation , starting from feature detection and ending by image registration . We used the well-known techniques ( e.g. SIFT , SURF , etc . ) for features detection and extraction in order to compare their performance . First , the deep neural network is constructed using several inceptions with different parallel convolutional operations which support reducing consuming time . Second , image augmentation of training dataset generates a larger data enough for deep neural network learning . This proposed task is aimed to address the small data problem . Based on feature extraction and classification with fully connected layers , we applied the Visual Geometry Group with 16 layers ( VGG16 ) model of convolutional neural networks to classify new images . Image-data augmentation in our model was performed using Keras ImageDataGenerator , and the leave-one-out procedure was used for model cross-validation . The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents . This conditional version of generative models is constructed by feeding data that we wish to condition on generator network and discriminator network in a GAN . The analysis has experimented on a high-resolution dataset called FMNIST across 60,000 samples of training images with reshaped image resolution size of $ 28^{\ast}28$. The following procedure is used for image dataset augmentation which improves the accuracy of image classifiers/segmentation techniques . The article underlines problems related to the feature dataset generation and presents an overview of current dataset augmentation methods such as image mirroring , cropping , rotating , shearing and color modification . New methods fot image dataset augmentation , such as utilization of CAD models and Deep Learning solutions , are also proposed . In order to ensure low cost , real time detection machine learning based Haar Cascade detector has been proposed and tested on a custom dataset specifically created for dataset augmentation methods evalutation . Since its foundation over 25 years ago , the Fraunhofer IIS has been involved in the applied research within the field of “ Visual Computing ” . This field of research incorporates all areas of image acquisition : including different kinds of imaging physics , various types of image sensors ; image formation : describing the manipulation of the acquired image data between the sensor and the output and 3-D-image reconstruction ; image coding and transportation : lossy and lossless coding , wire-based and wireless transmission ; image processing : image-to-image operations such as image enhancement , panoramic image stitching , or video tracking ; image analysis : pattern recognition , object detection , classification ; and image synthesis : including computer graphics , image augmentation and modeling . We present results on synthetic images showing the theoretical validity and performance of the method . We then use real data to demonstrate the potential of this approach for image augmentation applications in industrial maintenance assistance and architectural design . First , we introduce a deep architecture that is able to learn using a small number of training images and still achieves a high degree of generalization ability . Second , we strengthen the proposed DPU-Net by having a real-time augmentor control the image augmentation process . Our real-time augmentor contains specially-designed operations that simulate three types of IVUS artifacts and integrate them into the training images . We propose a deep feature consistent principle to measure the similarity between two images in feature space . Based on this principle , we investigate several traditional image processing problems for both image information augmentation ( companding and inverse halftoning ) and reduction ( downscaling , decolorization and HDR tone mapping ) . The experiments demonstrate the effectiveness of deep learning based solutions to solve these traditional low-level image processing problems . The challenges for image acquisition for Near-Infrared or Visual Wavelength lights under constrained and unconstrained environments have been considered here . The proposed system is divided into image preprocessing data augmentation followed by feature learning for classification components . In image preprocessing an annular iris , the portion is segmented out from an eyeball image and then transformed into a fixed-sized image region . As we show later , in our experiments , this choice of perturbation was able to improve the generalization performance . Random image augmentation is a variant of random perturbation that simply augments the dataset with images perturbed by regular deformation . 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 ] . Results show that participants preferred concepts that presented case-specific support , did not distract too much from the text , and gave details on demand . We conclude with design recommendations for designing text augmentation for language learning . The actual number of attendants along the day fluctuated from 8 to 12 , with an average of 9 attendants maintained almost all the time . The paper titled DeepSpot : Understanding Online Opinion Spam by Text Augmentation using Sentiment Encoder-Decoder Networks , by Avik Nayak , Haiquan Chen , Xiaojun Ruan , and Jinsong Ouyang was awarded the best paper award . The paper titled Scalable Community Detection over Geo-Social Network , by Xiuwen Zheng , Qiyu Liu , and Amarnath Gupta was awarded the best presentation award . In this paper , we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency , a task we call semantic text exchange . This is useful for text data augmentation and the semantic correction of text generated by chatbots and virtual assistants . We introduce a pipeline called SMERTI that combines entity replacement , similarity masking , and text infilling . Text augmentation research aims to enrich text dataset in order to gain higher performance compared to the one using original text dataset . We have conducted text augmentation process on Indonesian text classification by replacing certain words with their synonyms . The process consists of determining the number of words to be substituted in the sentence and selecting the substitute word from the synonym list . In contrast to data collection and annotation , data augmentation is a low cost way . In this paper , we propose a new method for text image augmentation . Different from traditional augmentation methods such as rotation , scaling and perspective transformation , our proposed augmentation method is designed to learn proper and efï¬cient data augmentation which is more effective and specific for training a robust recognizer .
In this work , we propose the expedited convolutional neural networks involving Tucker decomposition to recognize MCI using MRI images . We employ transfer learning and data augmentation to deal with limited training data . The effect of Tucker decomposition on saving computational time is discussed .
Then , we produce a large set of “ multi-class ” artificial samples , by interchanging the periocular and ocular parts from different subjects . These samples are used for data augmentation purposes and feed the learning phase of the CNN , always considering as label the ID of the periocular part . This way , for every periocular region , the CNN receives multiple samples of different ocular classes , forcing it to conclude that such regions should not be considered in its response .
However , poor performance of these methods can result from a lack of quantity or diversity in the data used to train the models . Recently , generative adversarial networks ( GANs ) have demonstrated the ability to generate synthetic images , which may be useful for data augmentation purposes . Here , we propose the application of GANs to construct chorio-retinal patches from OCT images which may be used to augment data for a patch-based approach to boundary segmentation .
We describe the initial version of VERIFAI which centers on simulation guided by formal models and specifications . Several use cases are illustrated with examples , including temporal-logic falsification , model-based systematic fuzz testing , parameter synthesis , counterexample analysis , and data set augmentation .
We introduce a forward simulation MCMC ( fsMCMC ) algorithm , which is primarily based upon simulation from the model . The fsMCMC algorithm formulates the simulation of the process explicitly as a data augmentation problem . By exploiting non-centred parameterizations , an efficient MCMC updating schema for the parameters and augmented data is introduced , whilst maintaining straightforward simulation from the model .
The images are modelized by hidden Markov fields with unknown parameters . Given the observed images , we give a Bayesian formulation and we propose to solve the resulting data augmentation problem by implementing a Monte Carlo Markov chain ( MCMC ) procedure . We separate the unknown variables into two categories : ( 1 ) the parameters of interest which are the mixing matrix , the noise covariance and the parameters of the sources distributions ; and ( 2 ) the hidden variables which are the unobserved sources and the unobserved pixels classification labels .
This paper presents a novel strategy that employs Generative Adversarial Networks ( GANs ) to augment data in the skin lesion segmentation task , which is a fundamental first step in the automated melanoma detection process . The proposed framework generates both skin lesion images and their segmentation masks , making the data augmentation process extremely straightforward . In order to thoroughly analyze how the quality and diversity of synthetic images impact the efficiency of the method , we remodel two different well known GANs : a Deep Convolutional GAN ( DCGAN ) and a Laplacian GAN ( LAPGAN ) .
Therefore , in this paper , we investigate and introduced a paradigm augmenting big data for IoV intelligent system to optimize massive data exploration in the field . The paradigm of big data augmentation is a systematic approach to development raises great expectations and concern to the analytical value of large-scale data that address IoV in the natural progression of intelligent IoV and cloud computing . The intelligent IoV technology is transforming to cloud system to satisfy a variety of IoV applications and user needs , which provide analytic and access of massive data .
In this paper , we focus on simple data-driven approach to solve deep learning based on implementing the Mask R-CNN module by analyzing deeper manipulation of datasets . We firstly approach to affine transformation and projective representation to data augmentation analysis in order to increasing large-scale data manually based on the state-of-the-art in views of computer vision . Then we evaluate our method concretely by connection our datasets by visualization data and completely in testing to many methods to understand intelligent data analysis in object detection and segmentation by using more than 5000 image according to many similar objects .
In this approach , the use of the 3-Dimensional Convolutional Neural Networks ( 3D-CNN ) is investigated using a multi-channel EEG data for emotion recognition . A data augmentation phase is developed to enhance the performance of the proposed 3D-CNN approach . And , a 3D data representation is formulated from the multi-channel EEG signals , which is used as data input for the proposed 3D-CNN model .
This is important because social media platforms are increasingly sharing their data and because of the recent rise of social media based on pictures . Pictorial data may soon become a viable option for data data-augmentation .
Instead , we keep the original aspect ratio of images fixed and resize them to five different scales ( setting their maximum of width or height to { 480 , 576 , 688 , 864 , 1200 } respectively ) as in [ reference ] . During training , we apply random horizontal flips to the images and select a scale at random as a form of jittering or data augmentation . At test time we average the outputs of 10 images ( i.e. the 5 scales and their flips ) .
The top of the network produces an embedding containing coarse and fine - grained information , so that images can be recognized based on the object class , particular object , or if they are distorted copies . Our joint training is simple : we minimize a cross - entropy loss for classification and a ranking loss that determines if two images are identical up to data augmentation , with no need for additional labels . A key component of MultiGrain is a pooling layer that takes advantage of high - resolution images with a network trained at a lower resolution .
appendix : Data augmentation hyper - parameters tab : full - data - augment gives the transformations in the full data augmentation used in our experiments ( sec : experimental - settings ) , along with their parameters . appendix : Additional results and ablation study for Multigrain in retrieval
These promising results are presented in this paper to demonstrate the potential benefits of the method ; clearly further innovations within the method are required if it is to be competitive on harder datasets like CIFAR - 10 , or Imagenet . We expect that the most likely avenues for improving our presented results for CIFAR - 10 , whilst retaining the method ’s core attributes , are ( 1 ) to introduce limited training of the Stage 1 filters by generalizing the method of ; ( 2 ) introduction of training data augmentation . We aim to pursuing these directions in our future work .
Note that the parameters of the batch normalisation modules are kept unchanged in the whole learning process . Online data - augmentation is performed during training to regularise the model . The training images are randomly mirrored , scaled by a ratio between 0.5 and 2 , rotated by an angle between - 10 and 10 degrees , translated by a random amount in the HSV colour space , and blurred with a randomly - sized Gaussian kernel , all on - the - fly .
In the following experiments , all models are trained using Adam , with a learning rate of 1e - 4 on a NVidia Titan X GPU . Leaf segmentation Since this dataset only consists of 128 images , we use online data augmentation to prevent the model from overfitting and to increase the overall robustness . We apply random left - right flip , random rotation with and random scale deformation with .
This is not specific to our approach , but a general problem in medical image analysis . One possible way of making the CNN invariant to the data heterogeneity is to learn a generative model for the data acquisition process , and use this model in the data augmentation step . This is a direction we explore as part of future work .
The representation used in is not invariant to graph isomorphism . Instead , this invariance must be learned by the downstream model through dataset augmentation . In addition to the eight MPNNâs discussed in Section [ reference ] there have been a number of other approaches to machine learning on graphical data which take advantage of the symmetries in a number of ways .
Among them , seq2seq models yuan2016grammatical , xie2016neural , ji2017nested , sakaguchi2017grammatical , schmaltz - EtAl:2017:EMNLP2017 , chollampatt2018 , junczys2018approaching have caught much attention . Unlike the models trained only with original error - corrected data , we propose a novel fluency boost learning mechanism for dynamic data augmentation along with training for GEC , despite some related studies that explore artificial error generation for GEC brockett2006correcting , foster2009generrate , rozovskaya2010training , rozovskaya2011algorithm , Rozovskaya2012The , felice - yuan:2014:SRW , xie2016neural , rei2017artificial , xie2018noising . Moreover , we propose fluency boost inference which allows the model to repeatedly edit a sentence as long as the sentence ’s fluency can be improved .
For regression problems at the leaf - nodes , we propose a generalized weighted ridge regression ( GWRR ) as an extension of the work in [ reference ] . GWRR models are generated based on the data distributions from the leaf - nodes . The main contribution of our method is on feature augmentation , so we call our method featureaugmented random forest ( FARF ) . The pipeline of our FARF method , which includes feature extraction , the training stage , and inference stages for SISR , is shown in Fig . 1 .
The main goal is to construct a full chain composed of 4 blocks that correspond to the main steps of augmented reality process : feature detection , feature extraction , feature matching and image registration . Our work proposes an improved technique for image augmentation , starting from feature detection and ending by image registration . We used the well-known techniques ( e.g. SIFT , SURF , etc . ) for features detection and extraction in order to compare their performance .
First , the deep neural network is constructed using several inceptions with different parallel convolutional operations which support reducing consuming time . Second , image augmentation of training dataset generates a larger data enough for deep neural network learning . This proposed task is aimed to address the small data problem .
Based on feature extraction and classification with fully connected layers , we applied the Visual Geometry Group with 16 layers ( VGG16 ) model of convolutional neural networks to classify new images . Image-data augmentation in our model was performed using Keras ImageDataGenerator , and the leave-one-out procedure was used for model cross-validation . The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents .
This conditional version of generative models is constructed by feeding data that we wish to condition on generator network and discriminator network in a GAN . The analysis has experimented on a high-resolution dataset called FMNIST across 60,000 samples of training images with reshaped image resolution size of $ 28^{\ast}28$. The following procedure is used for image dataset augmentation which improves the accuracy of image classifiers/segmentation techniques .
The article underlines problems related to the feature dataset generation and presents an overview of current dataset augmentation methods such as image mirroring , cropping , rotating , shearing and color modification . New methods fot image dataset augmentation , such as utilization of CAD models and Deep Learning solutions , are also proposed . In order to ensure low cost , real time detection machine learning based Haar Cascade detector has been proposed and tested on a custom dataset specifically created for dataset augmentation methods evalutation .
Since its foundation over 25 years ago , the Fraunhofer IIS has been involved in the applied research within the field of “ Visual Computing ” . This field of research incorporates all areas of image acquisition : including different kinds of imaging physics , various types of image sensors ; image formation : describing the manipulation of the acquired image data between the sensor and the output and 3-D-image reconstruction ; image coding and transportation : lossy and lossless coding , wire-based and wireless transmission ; image processing : image-to-image operations such as image enhancement , panoramic image stitching , or video tracking ; image analysis : pattern recognition , object detection , classification ; and image synthesis : including computer graphics , image augmentation and modeling .
We present results on synthetic images showing the theoretical validity and performance of the method . We then use real data to demonstrate the potential of this approach for image augmentation applications in industrial maintenance assistance and architectural design .
First , we introduce a deep architecture that is able to learn using a small number of training images and still achieves a high degree of generalization ability . Second , we strengthen the proposed DPU-Net by having a real-time augmentor control the image augmentation process . Our real-time augmentor contains specially-designed operations that simulate three types of IVUS artifacts and integrate them into the training images .
We propose a deep feature consistent principle to measure the similarity between two images in feature space . Based on this principle , we investigate several traditional image processing problems for both image information augmentation ( companding and inverse halftoning ) and reduction ( downscaling , decolorization and HDR tone mapping ) . The experiments demonstrate the effectiveness of deep learning based solutions to solve these traditional low-level image processing problems .
The challenges for image acquisition for Near-Infrared or Visual Wavelength lights under constrained and unconstrained environments have been considered here . The proposed system is divided into image preprocessing data augmentation followed by feature learning for classification components . In image preprocessing an annular iris , the portion is segmented out from an eyeball image and then transformed into a fixed-sized image region .
As we show later , in our experiments , this choice of perturbation was able to improve the generalization performance . Random image augmentation is a variant of random perturbation that simply augments the dataset with images perturbed by regular deformation . 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 ] .
Results show that participants preferred concepts that presented case-specific support , did not distract too much from the text , and gave details on demand . We conclude with design recommendations for designing text augmentation for language learning .
The actual number of attendants along the day fluctuated from 8 to 12 , with an average of 9 attendants maintained almost all the time . The paper titled DeepSpot : Understanding Online Opinion Spam by Text Augmentation using Sentiment Encoder-Decoder Networks , by Avik Nayak , Haiquan Chen , Xiaojun Ruan , and Jinsong Ouyang was awarded the best paper award . The paper titled Scalable Community Detection over Geo-Social Network , by Xiuwen Zheng , Qiyu Liu , and Amarnath Gupta was awarded the best presentation award .
In this paper , we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency , a task we call semantic text exchange . This is useful for text data augmentation and the semantic correction of text generated by chatbots and virtual assistants . We introduce a pipeline called SMERTI that combines entity replacement , similarity masking , and text infilling .
Text augmentation research aims to enrich text dataset in order to gain higher performance compared to the one using original text dataset . We have conducted text augmentation process on Indonesian text classification by replacing certain words with their synonyms . The process consists of determining the number of words to be substituted in the sentence and selecting the substitute word from the synonym list .
In contrast to data collection and annotation , data augmentation is a low cost way . In this paper , we propose a new method for text image augmentation . Different from traditional augmentation methods such as rotation , scaling and perspective transformation , our proposed augmentation method is designed to learn proper and efï¬cient data augmentation which is more effective and specific for training a robust recognizer .
Automatic Recognition of Mild Cognitive Impairment from MRI Images Using Expedited Convolutional Neural Networks Deep-PRWIS: Periocular Recognition Without the Iris and Sclera Using Deep Learning Frameworks Constructing Synthetic Chorio-Retinal Patches using Generative Adversarial Networks VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems Forward simulation MCMC with applications to stochastic epidemic models MCMC joint separation and segmentation of hidden Markov fields Augmenting data with GANs to segment melanoma skin lesions The Paradigm of Big Data for Augmenting Internet of Vehicle into the Intelligent Cloud Computing Systems Mask R-CNN with data augmentation for food detection and recognition EEG-Based Emotion Recognition using 3D Convolutional Neural Networks Evaluating the Added Value of Pictorial Data for Customer Churn Prediction Weakly Supervised Deep Detection Networks MultiGrain: a unified image embedding for classes and instances Enhanced image classification with a fast-learning shallow convolutional neural network Holistic, Instance-Level Human Parsing Semantic Instance Segmentation with a Discriminative Loss Function Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Neural Message Passing for Quantum Chemistry Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study Image Super-resolution via Feature-augmented Random Forest Augmented Reality application: A new implementation chain Hybrid discriminative models for banknote recognition and anti-counterfeit Development of a Deep-Learning-Based Artificial Intelligence Tool for Differential Diagnosis between Dry and Neovascular Age-Related Macular Degeneration Auxiliary Conditional Generative Adversarial Networks for Image Data Set Augmentation Data augmentation for haar cascade based automobile detection Visual Computing at the IIS: From Life Sciences to Industrial Applications Calibration propagation for image augmentation Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net. An investigation of deep learning for image processing applications Person identification using fusion of iris and periocular deep features Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Exploring visualizations for digital reading augmentation to support grammar learning 3rd ACM SIGSPATIAL Workshop on Analytics for Local Events and News (LENS 2019) Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange Effective Use of Augmentation Degree and Language Model for Synonym-based Text Augmentation on Indonesian Text Classification Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
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ICANN (1) DICTA IOV 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv 2015 International Joint Conference on Neural Networks (IJCNN) BMVC ArXiv Medical Image Anal. ICML ArXiv Signal Process. Image Commun. IWAR IEEE Transactions on Pattern Analysis and Machine Intelligence MUM EMNLP/IJCNLP (1)
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