Specifically , we propose a Generative Adversarial Networks for Femur Segmentation to map hip image to femur image with nonlinear relationship and produce accurate segmentation results . In Generative Adversarial Networks for Femur Segmentation , we use the adaptive contours estimation algorithm and absolute deviations loss function to optimize neural networks . Experimental results show that our method is accurate , robust , and achieves an average dice similarity coefficient of 0.995 . One of the challenges in the study of generative adversarial networks is the instability of its training . In this paper , we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator . In this paper we propose and evaluate an artificial intelligence method for pigmented and non-pigmented lesion segmentation . The method uses generative adversarial neural networks . The network was trained and tested on a large set of images acquired with smartphone cameras . Generative Adversarial Networks . Generative adversarial networks ( GANs ) have shown remarkable results in various computer vision tasks such as image generation , image translation , super - resolution imaging , and face image synthesis . A typical GAN model consists of two modules : a discriminator and a generator . Despite recent progress in generative image modeling , successfully generating high - resolution , diverse samples from complex datasets such as ImageNet remains an elusive goal . To this end , we train Generative Adversarial Networks at the largest scale yet attempted , and study the instabilities specific to such scale . We find that applying orthogonal regularization to the generator renders it amenable to a simple “ truncation trick , ” allowing fine control over the trade - off between sample fidelity and variety by reducing the variance of the Generator ’s input . We train a pair of encoder-decoder network to resolve depth information form image pairs and relative camera poses . To solve scale ambiguous of monocular sequences , a conditional generative adversarial network is applied . Experimental results show that the proposed method can overcome the problem of scale ambiguous and therefore is more suitable for a variety of applications . A conditional generative adversarial network , comprising a generator and discriminator networks , was trained using the motion-corrupted and corresponding ground truth ( original ) images as training pairs . RESULTS The images predicted by the conditional generative adversarial network have improved image quality compared to the motion-corrupted images . The mean absolute error between the motion-corrupted and ground-truth images of the test set was 16.4 % of the image mean value , whereas the mean absolute error between the conditional generative adversarial network-predicted and ground-truth images was 10.8 % The network output also demonstrated improved peak SNR and structural similarity index for all test-set images . However , speckle noise and low sampling rates have intensively reduced the quality of OCT images , which prevents the development of OCT-assisted diagnosis . Therefore , we propose a generative adversarial network-based approach ( named SDSR-OCT ) to simultaneously denoise and super-resolve OCT images . Moreover , we trained three different super-resolution models with different upscale factors ( 2 × , 4 × and 8 × ) to adapt to the corresponding downsampling rates . Digital halftone deals with transforming a gray scale image into its printable binary version . In this paper , a generative adversarial network-based model is proposed to perform both halftoning and its structural reconstruction . The GAN model is based on the concept of unpaired image to image translation and the model learns both transformations simultaneously . However , pan-sharpening approaches often encounter spectral distortion and detail distortion issues . In order to overcome the drawbacks of pan-sharpening methodologies , we propose an end-to-end pan-sharpening model consisting of an effective generative adversarial network architecture equipped with spatial feature transform layers that generate spatial detail features under spectral feature constraints . Through a large number of quantitative and visual assessments , we demonstrate that the proposed method achieves superior performance to other state-of-the-art methods . Specifically , a speech encoder is designed to represent the input speech signals as an embedding feature , and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes . Subsequently , a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature . Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation . Abstract Generative adversarial network ( GAN ) has shown great potential in infrared and visible image fusion . The existing GAN-based methods establish an adversarial game between generative image and source images to train the generator until the generative image contains enough meaningful information from source images . In particular , our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes ( i.e. , camera view , vehicle type and vehicle color ) . Moreover , to overcome the shortcomings of limited vehicle images of different views , we design a view-specified generative adversarial network to generate the multi-view vehicle images . For network training , we annotate the view labels on the VeRi-776 dataset . The BiGAN approach extends GANs to also learn the inverse mapping from the image data back into the latent space , and shows that this can learn features useful for image classification tasks . The conditional generative adversarial net ( CGAN ) is an extension of the GAN where both networks G and D receive an additional vector of information as input . This might contain , say , information about the class of the training example . In this paper , a new method for arrears prediction is proposed , which uses Wgan-Gp ( Improved Training of Wasserstein GANs ) to simulate the real data and then generate fake data to predict arrears with DBN . Electric power data ( 256 indicators ) , like the image pixel ( 16∗16 ) , is used as input of the Wgan-Gp to train the generator . For the first time , this paper convert the prediction of arrears time to prediction of arrears interval . To overcome these problems , we propose Conditional Wasserstein GAN- Gradient Penalty ( CWGAN-GP ) , a novel and efficient synthetic oversampling approach for imbalanced datasets , which can be constructed by adding auxiliary conditional information to the WGAN-GP . CWGAN-GP generates more realistic data and overcomes the aforementioned problems . Experiments on 15 different benchmarked datasets and two real imbalanced datasets empirically demonstrate that CWGAN-GP increases the quality of synthetic data ; furthermore , our approach outperforms the other oversampling approaches based on three evaluation metrics ( F-measure , G-mean , and the area under the receiver operating characteristic curve ) for five classifiers . It turns out that CVAE with L1 reconstruction loss and IVAE achieve the highest score in IS . CWGAN-GP with L1 tends to learn faster than CWGAN-GP , but IS does not increase from CWGAN-GP . CWGAN-GP tends to generate more diverse images than other models using reconstruction loss . A novel WGAN-GP-based model is proposed in this study to fulfill bi-directional synthesis of medical images for the first time . GMM-based noise generated from the Glow model is newly incorporated into the WGAN-GP-based model to better reflect the characteristics of heterogeneity commonly seen in medical images , which is beneficial to produce high-quality synthesized medical images . Both the conventional “ down-sampling”-like synthesis and the more challenging “ up-sampling”-like synthesis are realized through the newly introduced model , which is thoroughly evaluated with comparisons towards several popular deep learning-based models both qualitatively and quantitatively . By separating a feature classifier from the discriminator , the classifier can be trained with data augmentation technique , which can support to make a fine classifier . Evaluated with the CIFAR-10 dataset , ControlGAN outperforms AC-WGAN-GP which is an improved version of the ACGAN , where Inception score of the ControlGAN is 8.61 ± 0.10 . Furthermore , we demonstrate that the ControlGAN can generate intermediate features and opposite features for interpolated input and extrapolated input labels that are not used in the training process . We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation ( RSVP ) experiment and demonstrated the validity of the generated samples . We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet . Thus , how to use these un-annotated video slices to improve the DGACNN capability for recognizing FHD , in terms of both recognition accuracy and robustness , is very meaningful for FHD screening . The architecture of DGACNN comprises two parts , that is , DANomaly and GACNN ( Wgan-GP and CNN ) . DANomaly , similar to the ALOCC network , but incorporates cycle adversarial learning to train an end-to-end one-class classification ( OCC ) network that is more robust and has a higher accuracy than ALOCC in screening video slices . Thanks to transfer learning , the well-trained discriminator of WGAN-GP is used as a feature extractor for learning the spectral-spatial features without involving labelled samples . Moreover , in order to facilitate the optimization of WGAN-GP , we propose a novel dimensionality reduction ( DR ) method using convolutions to get lower-dimensional target data . Experimental results on real hyperspectral datasets demonstrate the efficiency of the proposed method combining the proposed DR and WGAN-GP(DR+WGAN-GP ) , which has great potential prospects in HSI classification . To address the challenge posed by limited labeled training samples , a semi-supervised Wasserstein Generative Adversarial Network with gradient penalty ( sWGAN-GP ) is proposed . The proposed sWGAN-GP includes a classifier with the shared architecture with the discriminator in GAN and its loss function enables the augmentation of limited training samples with generated EEG samples during training , thus resulting in improved classification performance . The several modeling challenges including frequency artifacts and training instability , are also considered . In this work , we develop a generative adversarial network ( GANs ) based data augmentation method to increase the size of children 's training data to improve speech recognition performance for children 's speech . Two different types of GANs are explored under WGAN-GP training framework , including the unconditional GANs with an unsupervised learning framework and the conditional GANs using acoustic states as conditions . The proposed data augmentation approaches are evaluated on a Mandarin speech recognition task , with only 40-hour children speech or further including 100-hour adult speech in the training . To learn disentangled representations , information - theoretic extension of GAN is proposed by maximizing the mutual information between a subset of noise variables and the generated results . In , WGAN and WGAN - GP minimize the Wasserstein - 1 distance between the generated distribution and the real distribution to improve the stability of learning generator . In this work , we adopt the WGAN framework to learn our DIAT model , and further suggest adaptive perceptual loss for identity - aware transfer and perceptual regularization to suppress visual artifacts . Generative Adversarial Networks ( GANs ) can learn data distributions without needing a parametric assumption on the output density . We show on a speech source separation experiment that , a multilayer perceptron trained with a Wasserstein-GAN formulation outperforms NMF , auto-encoders trained with maximum likelihood , and variational auto-encoders in terms of source to distortion ratio . In this paper , we present a multi-label learning algorithm to address the class imbalance and detection on ECGs with different durations . We utilize Deep Convolutional Generative Adversarial Networks ( DCGANs ) and Wasserstein GAN-Gradient Penalty ( WGAN-GP ) to generate new positive samples and use two losses to balance the importance between positive samples and negative samples . Moreover , we construct a Squeeze and Excitation-ResNet ( SE-ResNet ) module for normal rhythm and arrhythmia detection . Pre-training is to map source domain and target domain to a common feature space , and adversarial-training is to narrow down the gap between the mappings of the source and target domains on the common feature space . A Wasserstein GAN gradient penalty loss is applied to adversarial-training to guarantee the stability and convergence of the framework . We evaluate the framework on two public EEG datasets for emotion recognition , SEED and DEAP . We first show that samples generated with SampleRNN and WaveNet are unable to fool a CNN-based speaker recognition system . We propose a modification of the Wasserstein GAN objective function to make use of data that is real but not from the class being learned . Our semi-supervised learning method is able to perform both targeted and untargeted attacks , raising questions related to security in speaker authentication systems .
Specifically , we propose a Generative Adversarial Networks for Femur Segmentation to map hip image to femur image with nonlinear relationship and produce accurate segmentation results . In Generative Adversarial Networks for Femur Segmentation , we use the adaptive contours estimation algorithm and absolute deviations loss function to optimize neural networks . Experimental results show that our method is accurate , robust , and achieves an average dice similarity coefficient of 0.995 .
One of the challenges in the study of generative adversarial networks is the instability of its training . In this paper , we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator .
In this paper we propose and evaluate an artificial intelligence method for pigmented and non-pigmented lesion segmentation . The method uses generative adversarial neural networks . The network was trained and tested on a large set of images acquired with smartphone cameras .
Generative Adversarial Networks . Generative adversarial networks ( GANs ) have shown remarkable results in various computer vision tasks such as image generation , image translation , super - resolution imaging , and face image synthesis . A typical GAN model consists of two modules : a discriminator and a generator .
Despite recent progress in generative image modeling , successfully generating high - resolution , diverse samples from complex datasets such as ImageNet remains an elusive goal . To this end , we train Generative Adversarial Networks at the largest scale yet attempted , and study the instabilities specific to such scale . We find that applying orthogonal regularization to the generator renders it amenable to a simple “ truncation trick , ” allowing fine control over the trade - off between sample fidelity and variety by reducing the variance of the Generator ’s input .
We train a pair of encoder-decoder network to resolve depth information form image pairs and relative camera poses . To solve scale ambiguous of monocular sequences , a conditional generative adversarial network is applied . Experimental results show that the proposed method can overcome the problem of scale ambiguous and therefore is more suitable for a variety of applications .
A conditional generative adversarial network , comprising a generator and discriminator networks , was trained using the motion-corrupted and corresponding ground truth ( original ) images as training pairs . RESULTS The images predicted by the conditional generative adversarial network have improved image quality compared to the motion-corrupted images . The mean absolute error between the motion-corrupted and ground-truth images of the test set was 16.4 % of the image mean value , whereas the mean absolute error between the conditional generative adversarial network-predicted and ground-truth images was 10.8 % The network output also demonstrated improved peak SNR and structural similarity index for all test-set images .
However , speckle noise and low sampling rates have intensively reduced the quality of OCT images , which prevents the development of OCT-assisted diagnosis . Therefore , we propose a generative adversarial network-based approach ( named SDSR-OCT ) to simultaneously denoise and super-resolve OCT images . Moreover , we trained three different super-resolution models with different upscale factors ( 2 × , 4 × and 8 × ) to adapt to the corresponding downsampling rates .
Digital halftone deals with transforming a gray scale image into its printable binary version . In this paper , a generative adversarial network-based model is proposed to perform both halftoning and its structural reconstruction . The GAN model is based on the concept of unpaired image to image translation and the model learns both transformations simultaneously .
However , pan-sharpening approaches often encounter spectral distortion and detail distortion issues . In order to overcome the drawbacks of pan-sharpening methodologies , we propose an end-to-end pan-sharpening model consisting of an effective generative adversarial network architecture equipped with spatial feature transform layers that generate spatial detail features under spectral feature constraints . Through a large number of quantitative and visual assessments , we demonstrate that the proposed method achieves superior performance to other state-of-the-art methods .
Specifically , a speech encoder is designed to represent the input speech signals as an embedding feature , and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes . Subsequently , a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature . Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation .
Abstract Generative adversarial network ( GAN ) has shown great potential in infrared and visible image fusion . The existing GAN-based methods establish an adversarial game between generative image and source images to train the generator until the generative image contains enough meaningful information from source images .
In particular , our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes ( i.e. , camera view , vehicle type and vehicle color ) . Moreover , to overcome the shortcomings of limited vehicle images of different views , we design a view-specified generative adversarial network to generate the multi-view vehicle images . For network training , we annotate the view labels on the VeRi-776 dataset .
The BiGAN approach extends GANs to also learn the inverse mapping from the image data back into the latent space , and shows that this can learn features useful for image classification tasks . The conditional generative adversarial net ( CGAN ) is an extension of the GAN where both networks G and D receive an additional vector of information as input . This might contain , say , information about the class of the training example .
In this paper , a new method for arrears prediction is proposed , which uses Wgan-Gp ( Improved Training of Wasserstein GANs ) to simulate the real data and then generate fake data to predict arrears with DBN . Electric power data ( 256 indicators ) , like the image pixel ( 16∗16 ) , is used as input of the Wgan-Gp to train the generator . For the first time , this paper convert the prediction of arrears time to prediction of arrears interval .
To overcome these problems , we propose Conditional Wasserstein GAN- Gradient Penalty ( CWGAN-GP ) , a novel and efficient synthetic oversampling approach for imbalanced datasets , which can be constructed by adding auxiliary conditional information to the WGAN-GP . CWGAN-GP generates more realistic data and overcomes the aforementioned problems . Experiments on 15 different benchmarked datasets and two real imbalanced datasets empirically demonstrate that CWGAN-GP increases the quality of synthetic data ; furthermore , our approach outperforms the other oversampling approaches based on three evaluation metrics ( F-measure , G-mean , and the area under the receiver operating characteristic curve ) for five classifiers .
It turns out that CVAE with L1 reconstruction loss and IVAE achieve the highest score in IS . CWGAN-GP with L1 tends to learn faster than CWGAN-GP , but IS does not increase from CWGAN-GP . CWGAN-GP tends to generate more diverse images than other models using reconstruction loss .
A novel WGAN-GP-based model is proposed in this study to fulfill bi-directional synthesis of medical images for the first time . GMM-based noise generated from the Glow model is newly incorporated into the WGAN-GP-based model to better reflect the characteristics of heterogeneity commonly seen in medical images , which is beneficial to produce high-quality synthesized medical images . Both the conventional “ down-sampling”-like synthesis and the more challenging “ up-sampling”-like synthesis are realized through the newly introduced model , which is thoroughly evaluated with comparisons towards several popular deep learning-based models both qualitatively and quantitatively .
By separating a feature classifier from the discriminator , the classifier can be trained with data augmentation technique , which can support to make a fine classifier . Evaluated with the CIFAR-10 dataset , ControlGAN outperforms AC-WGAN-GP which is an improved version of the ACGAN , where Inception score of the ControlGAN is 8.61 ± 0.10 . Furthermore , we demonstrate that the ControlGAN can generate intermediate features and opposite features for interpolated input and extrapolated input labels that are not used in the training process .
We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation ( RSVP ) experiment and demonstrated the validity of the generated samples . We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet .
Thus , how to use these un-annotated video slices to improve the DGACNN capability for recognizing FHD , in terms of both recognition accuracy and robustness , is very meaningful for FHD screening . The architecture of DGACNN comprises two parts , that is , DANomaly and GACNN ( Wgan-GP and CNN ) . DANomaly , similar to the ALOCC network , but incorporates cycle adversarial learning to train an end-to-end one-class classification ( OCC ) network that is more robust and has a higher accuracy than ALOCC in screening video slices .
Thanks to transfer learning , the well-trained discriminator of WGAN-GP is used as a feature extractor for learning the spectral-spatial features without involving labelled samples . Moreover , in order to facilitate the optimization of WGAN-GP , we propose a novel dimensionality reduction ( DR ) method using convolutions to get lower-dimensional target data . Experimental results on real hyperspectral datasets demonstrate the efficiency of the proposed method combining the proposed DR and WGAN-GP(DR+WGAN-GP ) , which has great potential prospects in HSI classification .
To address the challenge posed by limited labeled training samples , a semi-supervised Wasserstein Generative Adversarial Network with gradient penalty ( sWGAN-GP ) is proposed . The proposed sWGAN-GP includes a classifier with the shared architecture with the discriminator in GAN and its loss function enables the augmentation of limited training samples with generated EEG samples during training , thus resulting in improved classification performance . The several modeling challenges including frequency artifacts and training instability , are also considered .
In this work , we develop a generative adversarial network ( GANs ) based data augmentation method to increase the size of children 's training data to improve speech recognition performance for children 's speech . Two different types of GANs are explored under WGAN-GP training framework , including the unconditional GANs with an unsupervised learning framework and the conditional GANs using acoustic states as conditions . The proposed data augmentation approaches are evaluated on a Mandarin speech recognition task , with only 40-hour children speech or further including 100-hour adult speech in the training .
To learn disentangled representations , information - theoretic extension of GAN is proposed by maximizing the mutual information between a subset of noise variables and the generated results . In , WGAN and WGAN - GP minimize the Wasserstein - 1 distance between the generated distribution and the real distribution to improve the stability of learning generator . In this work , we adopt the WGAN framework to learn our DIAT model , and further suggest adaptive perceptual loss for identity - aware transfer and perceptual regularization to suppress visual artifacts .
Generative Adversarial Networks ( GANs ) can learn data distributions without needing a parametric assumption on the output density . We show on a speech source separation experiment that , a multilayer perceptron trained with a Wasserstein-GAN formulation outperforms NMF , auto-encoders trained with maximum likelihood , and variational auto-encoders in terms of source to distortion ratio .
In this paper , we present a multi-label learning algorithm to address the class imbalance and detection on ECGs with different durations . We utilize Deep Convolutional Generative Adversarial Networks ( DCGANs ) and Wasserstein GAN-Gradient Penalty ( WGAN-GP ) to generate new positive samples and use two losses to balance the importance between positive samples and negative samples . Moreover , we construct a Squeeze and Excitation-ResNet ( SE-ResNet ) module for normal rhythm and arrhythmia detection .
Pre-training is to map source domain and target domain to a common feature space , and adversarial-training is to narrow down the gap between the mappings of the source and target domains on the common feature space . A Wasserstein GAN gradient penalty loss is applied to adversarial-training to guarantee the stability and convergence of the framework . We evaluate the framework on two public EEG datasets for emotion recognition , SEED and DEAP .
We first show that samples generated with SampleRNN and WaveNet are unable to fool a CNN-based speaker recognition system . We propose a modification of the Wasserstein GAN objective function to make use of data that is real but not from the class being learned . Our semi-supervised learning method is able to perform both targeted and untargeted attacks , raising questions related to security in speaker authentication systems .
Segmentation of Femurs in X-ray Image with Generative Adversarial Networks Spectral Normalization for Generative Adversarial Networks Generative Adversarial Neural Networks for Pigmented and Non-Pigmented Skin Lesions Detection in Clinical Images StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation Large Scale GAN Training for High Fidelity Natural Image Synthesis Scale Invariant Multi-view Depth Estimation Network with cGAN Refinement Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network. H-GAN: Deep Learning Model for Halftoning and its Reconstruction SFTGAN: a generative adversarial network for pan-sharpening equipped with spatial feature transform layers Direct Speech-to-Image Translation Infrared and visible image fusion using dual discriminators generative adversarial networks with Wasserstein distance Attributes Guided Feature Learning for Vehicle Re-identification Adversarial Discriminative Domain Adaptation Arrears prediction for electricity customer through Wgan-Gp Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification Sampling Using Neural Networks for colorizing the grayscale images Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation Controllable Generative Adversarial Network Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning Hyperspectral image classification with unsupervised feature extraction A Semi-Supervised Wasserstein Generative Adversarial Network for Classifying Driving Fatigue from EEG signals GANs for Children: A Generative Data Augmentation Strategy for Children Speech Recognition Deep Identity-aware Transfer of Facial Attributes Generative Adversarial Source Separation A Multi-label Learning Method to Detect Arrhythmia Based on 12-Lead ECGs WGAN Domain Adaptation for EEG-Based Emotion Recognition Attacking Speaker Recognition With Deep Generative Models
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CSCS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICLR ICS ICCE 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) MLMI@MICCAI SMC ASRU ArXiv MLMECH/CVII-STENT@MICCAI ICONIP (5)
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