N-Phenyl maleimide with an isocyanato group was synthesized and employed in the coupling reaction with 4-(nitrophenyl)-L-prolinol ( NPP ) . The radical homopolymerization and copolymerization with methyl methacrylate ( NIMA ) of the resulting maleimide were carried out to produce poly(N-phenyl maleimide ) and poly[(N-phenyl maleimide-co-methyl methacrylate ) ] containing NPP units through urethane linkages . The glass transition temperature of the copolymer ( 60 % NB4A composition ) was found to be 161/spl deg/C in the DSC thermogram . This direct connection undermines the modularity in the generic test architecture by tightly coupling its elements . In this paper , we propose a network-oriented indirect and modular architecture ( NIMA ) for postfabrication test in an SoC design methodology . In NIMA , test stimuli and expected results for digital cores are first compiled into new formats and subsequently encapsulated into packets . These packets are augmented with control and address bits such that they can autonomously be transmitted to their destination through a switching fabric . Since 1985 , the National Imagery and Mapping Agency ( NIMA ) ( formerly the Defense Mapping Agency ) has operated a network of highly-reliable GPS tracking stations to collect data for computation of the NIMA GPS precise ephemerides and surveying applications . Applied Research Laboratories , The University of Texas at Austin ( ARL : UT ) has been the life-cycle development and sustainment laboratory for the network throughout that time . In addition , the enhancements will include integration into customer 's systems for a variety of GPS applications . This presentation and paper will provide an overview of the current and planned configuration of the NIMA network and the integration of this network with customer systems . Topics addressed include the architecture of the network showing its flexibility in the addressing of current and future customer needs , a summary of the data quantities provided by the network , past and projected reliability of the network , quality assurance methods , and a summary of customers showing the data types received . BRIEF HISTORY OF THE NIMA GPS MONITOR STATION NETWORK The National Imagery and Mapping Agency ( NIMA ) GPS Monitor Station Network ( MSN ) was established in 1985 by one of the NIMA predecessor agencies , the Defense Mapping Agency ( DMA ) . The primary mission of the MSN is to collect observations of the GPS satellite constellation . Since the initial development , Applied Research Laboratories , The University of Texas at Austin ( ARL : UT ) has served as the life-cycle development facility for the network . The NIMA Network Control Center ( NCC ) operates the network and provides data to NIMA customers . ARL : UT is responsible for the design , development , and maintenance of the network components and for monitoring long-term trends in network performance . Profiler spectra are often contaminated by a variety of sources including aircraft , birds , velocities exceeding the Nyquist velocity , radio frequency interference , and ground clutter . The NIMA method uses mathematical analysis , fuzzy logic synthesis , and global image processing algorithms to mimic human experts ’ ability to identify atmospheric signals in the presence of such contaminants . NIMA is configurable and its processing can be tuned to optimize performance for a given profiler site . A simulation study is used to compare NIMA performance with that of a simple peak picking algorithm in the presence of ground clutter , RFI , and point targets . Some performance results for the NIMA confidence algorithm are also given . Three different test access mechanisms ( TAMs ) are compared on this platform — a serial threading approach analogous to the IEEE1149.1 standard , a test-rail approach , and a packet-switching test network ( NIMA ) . The NIMA architecture is the most effective when there is a limited number of I/O pins and could potentially become the fastest architecture when it is allowed to operate at higher clock speed than the core DFT . However , the test rail approach is the most effective of the three TAMs if the test width is not restricted or if the area overhead is critical . This characteristics were correlated with data concerning the animal , such as : side of hooves , number of navicular syndrome ( scale 0 - 3 ) , type , sex , age , weight , information about lace , information about heel . This paper shows the introduction to the study of use the neural image analysis in the diagnosis of navicular bone syndrome . Prepared method can provide an introduction to the study of non-invasive way to assess the condition of the horse navicular bone . The purpose of the neural image analysis is to trace the velocities and the directions of moving mitochondria migrating through axons . This paper proposes an automated technique for detecting axon structure . The paper presents the definition of a neural image processing system to be used with intensified CCD-based detectors for photon counting applications . It consists of 25 neurons linked to a reconfigurable network . The concept is derived from sociology . We then provide neural image processing algorithms suitable for CNN with the small world network model . Special attention has been given to typical neural algorithms for image processing , such as noise reduction and edge extraction . This paper aimed to identify the different levels of sequential features with respect to various contexts to evaluate the speaker 's proficiency of a language . We have employed Neural Network based automated assessment . We have combined 3 attention based Bidirectional Long-Short-Term-Memory to effectively consider the three dimensions of a speech as delivery , grammar and content . Author(s ) : JIANG , ZHENLI | Advisor(s ) : WU , YINGNIAN | Abstract : We present an application of conditional generative adversarial network ( cGAN ) to produce photo-realistic portraits based on human face sketches . Our basic U-Net and PatchGAN model architecture is from pix2pix GAN . U-Net is agenerator that skips connections between each layer i and layer n-i of neural networks to preserve lower-layer information between inputs and outputs , and PatchGAN is a discriminator modeling on small patches of images to force high-frequency correctness . Based on U-Net and PatchGAN , we tried different loss functions , including L1+cGAN , L2+cGAN , L1+GAN and L1+WGAN . In this work , we investigate the feasibility of generating a 2D echo view using another view based on adversarial generative models . The objective optimized to train the view-conversion model is based on the ideas introduced by LSGAN , PatchGAN and Conditional GAN ( cGAN ) . The size and length of the left ventricle in the generated target echo view is compared against that of the target ground-truth to assess the validity of the echo view conversion . In this paper , we study super-resolution for ODIs and propose an improved generative adversarial network based model which is optimized to handle the artifacts obtained in the spherical observational space . Specifically , we propose to use a fast PatchGAN discriminator , as it needs fewer parameters and improves the super-resolution at a fine scale . We also explore the generative models with adversarial learning by introducing a spherical-content specific loss function , called 360-SS . To that end , we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term . Extending the well- established generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss , the proposed steerable GAN ( sGAN ) method is evaluated on the large public database IXI . Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values , while it leads to improved visual perceptual quality . In order to improve the image haze removal level , this paper proposes a single image haze removal method based on network image translation ( Pix2Pix ) . Relying on the encoder-decoder model and the Markov Discriminator ( PatchGAN ) , it is possible to maintain a high resolution and texture structure of the image while defogging . Finally , experiments on synthetic prove that this algorithm is better than others in objective indicators of PSNR and SSIM . The generative network combines low-frequency contents and high-frequency details of a target to boost the performance of the deceptive jamming template generation . Meanwhile , the discriminative network adopts the PatchGAN architecture to capture local texture statistics to improve the fidelity of the deceptive jamming template . Several real datasets are utilized to verify the effectiveness and superiority of the proposed DJTGAN . For the generator , a “ U-Net”-based architecture is used . For the discriminator , a convolutional “ PatchGAN ” classifier is used , which only penalizes structure at the scale of image patches . They have shown that this approach is effective on a wider range of problems . Unlike past work , for our generator we use a “ U - Net” - based architecture , and for our discriminator we use a convolutional “ PatchGAN ” classifier , which only penalizes structure at the scale of image patches . A similar PatchGAN architecture was previously proposed in to capture local style statistics . Here we show that this approach is effective on a wider range of problems , and we investigate the effect of changing the patch size . Each skip connection simply concatenates all channels at layer with those at layer . subsubsection : Markovian discriminator ( PatchGAN ) It is well known that the L2 loss – and L1 , see Figure [ reference ] Same as BicycleGAN , we use a U - Net network for the generator , and inject the latent codes into every layer of the generator . The architecture of the discriminator is a two - scale PatchGAN network . For the training , both Pix2Pix and MSGAN framework use the same hyper - parameters as the officially released version . Abstract An inexpensive artifact suppressor/signal inverter has been developed for use with commercially available window discriminators in unit recordings . This device provides a triggered blockade of the signal to prevent erroneous triggering of the window discriminator by the stimulus artifact . The unit is also capable of inverting the signal for easier unit discrimination when the positive component is too close to baseline or there is more than one positive component present . The adversarial setting is employed in both training steps . However , with the aim of avoiding known training instabilities in GANs , a multiple discriminator approach is used to train both models . Results in the studied video dataset indicate that , by employing such an approach , the recurrent part is able to learn how to coherently navigate the image manifold induced by the frames generator , thus yielding more natural-looking scenes . The least squares error resulting from the Kalman filter-estimated hyperspectral signature is then used as the power for hyperspectral signature similarity and discrimination . Experimental results demonstrate that such Kalman filter-based discriminators are more effective than commonly used spectral similarity measures such as spectral angle mapper ( SAM ) or Euclidean distance . The delay lock loop ( DLL ) is a well-known technique to track the codes for GNSS spread spectrum systems . This paper also presents non-coherent square law DLLs and the impacts of some parameters on DLL discriminators such as number of samples per chip , early-late spacing , different C/ No values where C denotes the signal power and No is the noise spectral density , and the impact of with or without frontend device . It is expected that this ASIC would find wider application with other detector solutions as well . Each channel is composed by a charge sensitive preamplifier , a shaping amplifier , two twin discriminators for photon counting within an energy window and one for detection of very small signals , e.g. due to inter-strip charge sharing effect . The chip can accommodate energies ranging between 0 to 30 keV. These results also support the claim that weaker discriminators have higher entropy improving modes coverage . Keywords : multiple discriminators , curriculum learning , multiple resolutions discriminators , multi-armed bandits , generative adversarial networks , smooth discriminators , multi-discriminator gan training , multiple experts . We propose to incorporate adversarial dropout in generative multi-adversarial networks , by omitting or dropping out , the feedback of each discriminator in the framework with some probability at the end of each batch . Our approach forces the single generator not to constrain its output to satisfy a single discriminator , but , instead , to satisfy a dynamic ensemble of discriminators . We show that this leads to a more generalized generator , promoting variety in the generated samples and avoiding the common mode collapse problem commonly experienced with generative adversarial networks ( GANs ) . In statistical pattern recognition , problems involve distinguishing of various concepts or classes , based on the development of classifiers/discriminators . These exploit discriminatory information existing in measurements originating from objects . We show that unconstrained video discriminator architectures induce a loss surface with high curvature which make optimisation difficult . We also show that this curvature becomes more extreme as the maximal kernel dimension of video discriminators increases . With these observations in hand , we propose a family of efficient Lower-Dimensional Video Discriminators for GANs ( LDVD GANs ) . In this paper , we propose the text-adaptive generative adversarial network ( TAGAN ) to generate semantically manipulated images while preserving text-irrelevant contents . The key to our method is the text-adaptive discriminator that creates word level local discriminators according to input text to classify fine-grained attributes independently . With this discriminator , the generator learns to generate images where only regions that correspond to the given text is modified . To establish the generality of the proposed techniques , we show qualitative results of our model in speech synthesis , music domain translation and unconditional music synthesis . We evaluate the various components of the model through ablation studies and suggest a set of guidelines to design general purpose discriminators and generators for conditional sequence synthesis tasks . Our model is non-autoregressive , fully convolutional , with significantly fewer parameters than competing models and generalizes to unseen speakers for mel-spectrogram inversion . We replace with in Eq . ( [ reference ] ) and train the encoder jointly with the generators and discriminators . After the encoder is trained , we run it on all instances in the training images and record the obtained features . Next , we perform pixel - level adaptation using our image space GAN losses together with semantic consistency and cycle consistency losses . This yeilds learned parameters for the image transformations , and , image discriminators , and , as well as an initial setting of the task model , , which is trained using pixel transformed source images and the corresponding source pixel labels . Finally , we perform feature space adpatation in order to update the target semantic model , , to have features which are aligned between the source images mapped into target style and the real target images . For feature space adaptation we use learning rate 1e - 5 and train for max 200 epochs over the data . For pixel space adaptation we train our generators and discriminators with equal weighting on all losses , use batch size 100 , learning rate 2e - 4 ( default from CycleGAN ) , and trained for 50 epochs . We ran each experiment 4 times and report the average and standard error across the runs . A → B such that the distribution of images from G ( A ) is indistinguishable from the distribution B using an adversarial loss . CycleGAN contains two mapping functions G : A → B and F : B → A. Two adversarial discriminators D A and D B are proposed to distinguish whether images are translated from another domain . CycleGAN applies the GAN framework to jointly train the generative and discriminative models . Our goal is to learn a multimodal mapping between two visual domains and without paired training data . As illustrated in Figure [ reference ] , our framework consists of content encoders , attribute encoders , generators , and domain discriminators for both domains , and a content discriminators . Take domain as an example , the content encoder maps images onto a shared , domain - invariant content space ( ) and the attribute encoder maps images onto a domain - specific attribute space ( ) .
N-Phenyl maleimide with an isocyanato group was synthesized and employed in the coupling reaction with 4-(nitrophenyl)-L-prolinol ( NPP ) . The radical homopolymerization and copolymerization with methyl methacrylate ( NIMA ) of the resulting maleimide were carried out to produce poly(N-phenyl maleimide ) and poly[(N-phenyl maleimide-co-methyl methacrylate ) ] containing NPP units through urethane linkages . The glass transition temperature of the copolymer ( 60 % NB4A composition ) was found to be 161/spl deg/C in the DSC thermogram .
This direct connection undermines the modularity in the generic test architecture by tightly coupling its elements . In this paper , we propose a network-oriented indirect and modular architecture ( NIMA ) for postfabrication test in an SoC design methodology . In NIMA , test stimuli and expected results for digital cores are first compiled into new formats and subsequently encapsulated into packets . These packets are augmented with control and address bits such that they can autonomously be transmitted to their destination through a switching fabric .
Since 1985 , the National Imagery and Mapping Agency ( NIMA ) ( formerly the Defense Mapping Agency ) has operated a network of highly-reliable GPS tracking stations to collect data for computation of the NIMA GPS precise ephemerides and surveying applications . Applied Research Laboratories , The University of Texas at Austin ( ARL : UT ) has been the life-cycle development and sustainment laboratory for the network throughout that time .
In addition , the enhancements will include integration into customer 's systems for a variety of GPS applications . This presentation and paper will provide an overview of the current and planned configuration of the NIMA network and the integration of this network with customer systems . Topics addressed include the architecture of the network showing its flexibility in the addressing of current and future customer needs , a summary of the data quantities provided by the network , past and projected reliability of the network , quality assurance methods , and a summary of customers showing the data types received . BRIEF HISTORY OF THE NIMA GPS MONITOR STATION NETWORK The National Imagery and Mapping Agency ( NIMA ) GPS Monitor Station Network ( MSN ) was established in 1985 by one of the NIMA predecessor agencies , the Defense Mapping Agency ( DMA ) . The primary mission of the MSN is to collect observations of the GPS satellite constellation .
Since the initial development , Applied Research Laboratories , The University of Texas at Austin ( ARL : UT ) has served as the life-cycle development facility for the network . The NIMA Network Control Center ( NCC ) operates the network and provides data to NIMA customers . ARL : UT is responsible for the design , development , and maintenance of the network components and for monitoring long-term trends in network performance .
Profiler spectra are often contaminated by a variety of sources including aircraft , birds , velocities exceeding the Nyquist velocity , radio frequency interference , and ground clutter . The NIMA method uses mathematical analysis , fuzzy logic synthesis , and global image processing algorithms to mimic human experts ’ ability to identify atmospheric signals in the presence of such contaminants . NIMA is configurable and its processing can be tuned to optimize performance for a given profiler site .
A simulation study is used to compare NIMA performance with that of a simple peak picking algorithm in the presence of ground clutter , RFI , and point targets . Some performance results for the NIMA confidence algorithm are also given .
Three different test access mechanisms ( TAMs ) are compared on this platform — a serial threading approach analogous to the IEEE1149.1 standard , a test-rail approach , and a packet-switching test network ( NIMA ) . The NIMA architecture is the most effective when there is a limited number of I/O pins and could potentially become the fastest architecture when it is allowed to operate at higher clock speed than the core DFT . However , the test rail approach is the most effective of the three TAMs if the test width is not restricted or if the area overhead is critical .
This characteristics were correlated with data concerning the animal , such as : side of hooves , number of navicular syndrome ( scale 0 - 3 ) , type , sex , age , weight , information about lace , information about heel . This paper shows the introduction to the study of use the neural image analysis in the diagnosis of navicular bone syndrome . Prepared method can provide an introduction to the study of non-invasive way to assess the condition of the horse navicular bone .
The purpose of the neural image analysis is to trace the velocities and the directions of moving mitochondria migrating through axons . This paper proposes an automated technique for detecting axon structure .
The paper presents the definition of a neural image processing system to be used with intensified CCD-based detectors for photon counting applications . It consists of 25 neurons linked to a reconfigurable network .
The concept is derived from sociology . We then provide neural image processing algorithms suitable for CNN with the small world network model . Special attention has been given to typical neural algorithms for image processing , such as noise reduction and edge extraction .
This paper aimed to identify the different levels of sequential features with respect to various contexts to evaluate the speaker 's proficiency of a language . We have employed Neural Network based automated assessment . We have combined 3 attention based Bidirectional Long-Short-Term-Memory to effectively consider the three dimensions of a speech as delivery , grammar and content .
Author(s ) : JIANG , ZHENLI | Advisor(s ) : WU , YINGNIAN | Abstract : We present an application of conditional generative adversarial network ( cGAN ) to produce photo-realistic portraits based on human face sketches . Our basic U-Net and PatchGAN model architecture is from pix2pix GAN . U-Net is agenerator that skips connections between each layer i and layer n-i of neural networks to preserve lower-layer information between inputs and outputs , and PatchGAN is a discriminator modeling on small patches of images to force high-frequency correctness . Based on U-Net and PatchGAN , we tried different loss functions , including L1+cGAN , L2+cGAN , L1+GAN and L1+WGAN .
In this work , we investigate the feasibility of generating a 2D echo view using another view based on adversarial generative models . The objective optimized to train the view-conversion model is based on the ideas introduced by LSGAN , PatchGAN and Conditional GAN ( cGAN ) . The size and length of the left ventricle in the generated target echo view is compared against that of the target ground-truth to assess the validity of the echo view conversion .
In this paper , we study super-resolution for ODIs and propose an improved generative adversarial network based model which is optimized to handle the artifacts obtained in the spherical observational space . Specifically , we propose to use a fast PatchGAN discriminator , as it needs fewer parameters and improves the super-resolution at a fine scale . We also explore the generative models with adversarial learning by introducing a spherical-content specific loss function , called 360-SS .
To that end , we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term . Extending the well- established generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss , the proposed steerable GAN ( sGAN ) method is evaluated on the large public database IXI . Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values , while it leads to improved visual perceptual quality .
In order to improve the image haze removal level , this paper proposes a single image haze removal method based on network image translation ( Pix2Pix ) . Relying on the encoder-decoder model and the Markov Discriminator ( PatchGAN ) , it is possible to maintain a high resolution and texture structure of the image while defogging . Finally , experiments on synthetic prove that this algorithm is better than others in objective indicators of PSNR and SSIM .
The generative network combines low-frequency contents and high-frequency details of a target to boost the performance of the deceptive jamming template generation . Meanwhile , the discriminative network adopts the PatchGAN architecture to capture local texture statistics to improve the fidelity of the deceptive jamming template . Several real datasets are utilized to verify the effectiveness and superiority of the proposed DJTGAN .
For the generator , a “ U-Net”-based architecture is used . For the discriminator , a convolutional “ PatchGAN ” classifier is used , which only penalizes structure at the scale of image patches . They have shown that this approach is effective on a wider range of problems .
Unlike past work , for our generator we use a “ U - Net” - based architecture , and for our discriminator we use a convolutional “ PatchGAN ” classifier , which only penalizes structure at the scale of image patches . A similar PatchGAN architecture was previously proposed in to capture local style statistics . Here we show that this approach is effective on a wider range of problems , and we investigate the effect of changing the patch size .
Each skip connection simply concatenates all channels at layer with those at layer . subsubsection : Markovian discriminator ( PatchGAN ) It is well known that the L2 loss – and L1 , see Figure [ reference ]
Same as BicycleGAN , we use a U - Net network for the generator , and inject the latent codes into every layer of the generator . The architecture of the discriminator is a two - scale PatchGAN network . For the training , both Pix2Pix and MSGAN framework use the same hyper - parameters as the officially released version .
Abstract An inexpensive artifact suppressor/signal inverter has been developed for use with commercially available window discriminators in unit recordings . This device provides a triggered blockade of the signal to prevent erroneous triggering of the window discriminator by the stimulus artifact . The unit is also capable of inverting the signal for easier unit discrimination when the positive component is too close to baseline or there is more than one positive component present .
The adversarial setting is employed in both training steps . However , with the aim of avoiding known training instabilities in GANs , a multiple discriminator approach is used to train both models . Results in the studied video dataset indicate that , by employing such an approach , the recurrent part is able to learn how to coherently navigate the image manifold induced by the frames generator , thus yielding more natural-looking scenes .
The least squares error resulting from the Kalman filter-estimated hyperspectral signature is then used as the power for hyperspectral signature similarity and discrimination . Experimental results demonstrate that such Kalman filter-based discriminators are more effective than commonly used spectral similarity measures such as spectral angle mapper ( SAM ) or Euclidean distance .
The delay lock loop ( DLL ) is a well-known technique to track the codes for GNSS spread spectrum systems . This paper also presents non-coherent square law DLLs and the impacts of some parameters on DLL discriminators such as number of samples per chip , early-late spacing , different C/ No values where C denotes the signal power and No is the noise spectral density , and the impact of with or without frontend device .
It is expected that this ASIC would find wider application with other detector solutions as well . Each channel is composed by a charge sensitive preamplifier , a shaping amplifier , two twin discriminators for photon counting within an energy window and one for detection of very small signals , e.g. due to inter-strip charge sharing effect . The chip can accommodate energies ranging between 0 to 30 keV.
These results also support the claim that weaker discriminators have higher entropy improving modes coverage . Keywords : multiple discriminators , curriculum learning , multiple resolutions discriminators , multi-armed bandits , generative adversarial networks , smooth discriminators , multi-discriminator gan training , multiple experts .
We propose to incorporate adversarial dropout in generative multi-adversarial networks , by omitting or dropping out , the feedback of each discriminator in the framework with some probability at the end of each batch . Our approach forces the single generator not to constrain its output to satisfy a single discriminator , but , instead , to satisfy a dynamic ensemble of discriminators . We show that this leads to a more generalized generator , promoting variety in the generated samples and avoiding the common mode collapse problem commonly experienced with generative adversarial networks ( GANs ) .
In statistical pattern recognition , problems involve distinguishing of various concepts or classes , based on the development of classifiers/discriminators . These exploit discriminatory information existing in measurements originating from objects .
We show that unconstrained video discriminator architectures induce a loss surface with high curvature which make optimisation difficult . We also show that this curvature becomes more extreme as the maximal kernel dimension of video discriminators increases . With these observations in hand , we propose a family of efficient Lower-Dimensional Video Discriminators for GANs ( LDVD GANs ) .
In this paper , we propose the text-adaptive generative adversarial network ( TAGAN ) to generate semantically manipulated images while preserving text-irrelevant contents . The key to our method is the text-adaptive discriminator that creates word level local discriminators according to input text to classify fine-grained attributes independently . With this discriminator , the generator learns to generate images where only regions that correspond to the given text is modified .
To establish the generality of the proposed techniques , we show qualitative results of our model in speech synthesis , music domain translation and unconditional music synthesis . We evaluate the various components of the model through ablation studies and suggest a set of guidelines to design general purpose discriminators and generators for conditional sequence synthesis tasks . Our model is non-autoregressive , fully convolutional , with significantly fewer parameters than competing models and generalizes to unseen speakers for mel-spectrogram inversion .
We replace with in Eq . ( [ reference ] ) and train the encoder jointly with the generators and discriminators . After the encoder is trained , we run it on all instances in the training images and record the obtained features .
Next , we perform pixel - level adaptation using our image space GAN losses together with semantic consistency and cycle consistency losses . This yeilds learned parameters for the image transformations , and , image discriminators , and , as well as an initial setting of the task model , , which is trained using pixel transformed source images and the corresponding source pixel labels . Finally , we perform feature space adpatation in order to update the target semantic model , , to have features which are aligned between the source images mapped into target style and the real target images .
For feature space adaptation we use learning rate 1e - 5 and train for max 200 epochs over the data . For pixel space adaptation we train our generators and discriminators with equal weighting on all losses , use batch size 100 , learning rate 2e - 4 ( default from CycleGAN ) , and trained for 50 epochs . We ran each experiment 4 times and report the average and standard error across the runs .
A → B such that the distribution of images from G ( A ) is indistinguishable from the distribution B using an adversarial loss . CycleGAN contains two mapping functions G : A → B and F : B → A. Two adversarial discriminators D A and D B are proposed to distinguish whether images are translated from another domain . CycleGAN applies the GAN framework to jointly train the generative and discriminative models .
Our goal is to learn a multimodal mapping between two visual domains and without paired training data . As illustrated in Figure [ reference ] , our framework consists of content encoders , attribute encoders , generators , and domain discriminators for both domains , and a content discriminators . Take domain as an example , the content encoder maps images onto a shared , domain - invariant content space ( ) and the attribute encoder maps images onto a domain - specific attribute space ( ) .
Thermally stable maleimide polymers for second-order nonlinear optics Indirect test architecture for SoC testing Integration of a Real-Time GPS Data Collection Network with a Variety of Customer Applications The NIMA Method for Improved Moment Estimation from Doppler Spectra Novel Test Methodologies for SoC/IP Design Image acquisitions, processing and analysis in the process of obtaining characteristics of horse navicular bone An Automated Technique for Detecting Axon Structure in Time-Lapse Neural Image Sequence A reconfigurable neural network for pattern identification in CCD-based detectors Fault Tolerance in Small World Cellular Neural Networks for Image Processing Automated Language Scoring System by Employing Neural Network Approaches Automatic sketch to photo translation A Study into Echocardiography View Conversion Super-resolution of Omnidirectional Images Using Adversarial Learning Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI Patch-based Generative Adversarial Network for Single Image Haze Removal Deceptive jamming template synthesis for SAR based on generative adversarial nets Image quality enhancement of a CD-SEM image using conditional generative adversarial networks Image-to-Image Translation with Conditional Adversarial Networks Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis Inexpensive stimulus artifact suppressor/signal inverter for use while recording unit activity Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation Kalman filter-based approaches to hyperspectral signature similarity and discrimination Implementation Of Code And Carrier Tracking Loops For Software GPS Receivers Design of a multichannel low-power integrated circuit for microstrip detectors Online Adaptative Curriculum Learning for GANs Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators Operating characteristics for the design and optimisation of classification systems Lower Dimensional Kernels for Video Discriminators Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs CyCADA: Cycle-Consistent Adversarial Domain Adaptation Camera Style Adaptation for Person Re-identification Diverse Image-to-Image Translation via Disentangled Representations
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["Computer Science"] ["Computer Science"] ["Computer Science"] ["Physics"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science","Mathematics"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science","Engineering"] ["Psychology","Medicine"] ["Computer Science","Mathematics"] ["Physics","Engineering"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Engineering","Mathematics"] ["Computer Science"] ["Computer Science","Engineering"]
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ICDIP AINA Workshops (1) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) NeurIPS NeurIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICML 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ECCV
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