generative adversarial networks paper

11.95470 TL /CS /DeviceRGB /R8 55 0 R 11.95630 TL 7 0 obj [ <0263756c7479> -361.00300 (of) -360.01600 (intractable) -360.98100 (inference\054) -388.01900 (which) -360.98400 (in) -360.00900 (turn) -360.98400 (restricts) -361.01800 (the) ] TJ /ExtGState << >> /R133 220 0 R For many AI projects, deep learning techniques are increasingly being used as the building blocks for innovative solutions ranging from image classification to object detection, image segmentation, image similarity, and text analytics (e.g., sentiment analysis, key phrase extraction). 4.02227 -3.68828 Td Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in … Our method takes unpaired photos and cartoon images for training, which is easy to use. /F2 9 Tf That is, we utilize GANs to train a very powerful generator of facial texture in UV space. T* [ (to) -283 (the) -283.00400 (real) -283.01700 (data\056) -408.98600 (Based) -282.99700 (on) -283.00200 (this) -282.98700 (observ) 24.99090 (ation\054) -292.00500 (we) -283.01200 (propose) -282.99200 (the) ] TJ Unlike the CNN-based methods, FV-GAN learns from the joint distribution of finger vein images and … T* /R20 6.97380 Tf

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. /XObject << We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. /F1 198 0 R data synthesis using generative adversarial networks (GAN) and proposed various algorithms. ️ [Energy-based generative adversarial network] (Lecun paper) ️ [Improved Techniques for Training GANs] (Goodfellow's paper) ️ [Mode Regularized Generative Adversarial Networks] (Yoshua Bengio , ICLR 2017) ️ [Improving Generative Adversarial Networks with Denoising Feature Matching] /F2 43 0 R /ca 1 /R142 206 0 R 4 0 obj Please help contribute this list by contacting [Me][] or add pull request, ✔️ [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION], ✔️ [Image-to-image translation using conditional adversarial nets], ✔️ [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks], ✔️ [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks], ✔️ [CoGAN: Coupled Generative Adversarial Networks], ✔️ [Unsupervised Image-to-Image Translation with Generative Adversarial Networks], ✔️ [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation], ✔️ [Unsupervised Image-to-Image Translation Networks], ✔️ [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs], ✔️ [XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings], ✔️ [UNIT: UNsupervised Image-to-image Translation Networks], ✔️ [Toward Multimodal Image-to-Image Translation], ✔️ [Multimodal Unsupervised Image-to-Image Translation], ✔️ [Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation], ✔️ [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation], ✔️ [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation], ✔️ [StarGAN v2: Diverse Image Synthesis for Multiple Domains], ✔️ [Structural-analogy from a Single Image Pair], ✔️ [High-Resolution Daytime Translation Without Domain Labels], ✔️ [Rethinking the Truly Unsupervised Image-to-Image Translation], ✔️ [Diverse Image Generation via Self-Conditioned GANs], ✔️ [Contrastive Learning for Unpaired Image-to-Image Translation], ✔️ [Autoencoding beyond pixels using a learned similarity metric], ✔️ [Coupled Generative Adversarial Networks], ✔️ [Invertible Conditional GANs for image editing], ✔️ [Learning Residual Images for Face Attribute Manipulation], ✔️ [Neural Photo Editing with Introspective Adversarial Networks], ✔️ [Neural Face Editing with Intrinsic Image Disentangling], ✔️ [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ], ✔️ [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis], ✔️ [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation], ✔️ [Arbitrary Facial Attribute Editing: Only Change What You Want], ✔️ [ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes], ✔️ [Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation], ✔️ [GANimation: Anatomically-aware Facial Animation from a Single Image], ✔️ [Geometry Guided Adversarial Facial Expression Synthesis], ✔️ [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing], ✔️ [3d guided fine-grained face manipulation] [Paper](CVPR 2019), ✔️ [SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color], ✔️ [A Survey of Deep Facial Attribute Analysis], ✔️ [PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing], ✔️ [SSCGAN: Facial Attribute Editing via StyleSkip Connections], ✔️ [CAFE-GAN: Arbitrary Face Attribute Editingwith Complementary Attention Feature], ✔️ [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks], ✔️ [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks], ✔️ [Generative Adversarial Text to Image Synthesis], ✔️ [Improved Techniques for Training GANs], ✔️ [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space], ✔️ [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks], ✔️ [Improved Training of Wasserstein GANs], ✔️ [Boundary Equibilibrium Generative Adversarial Networks], ✔️ [Progressive Growing of GANs for Improved Quality, Stability, and Variation], ✔️ [ Self-Attention Generative Adversarial Networks ], ✔️ [Large Scale GAN Training for High Fidelity Natural Image Synthesis], ✔️ [A Style-Based Generator Architecture for Generative Adversarial Networks], ✔️ [Analyzing and Improving the Image Quality of StyleGAN], ✔️ [SinGAN: Learning a Generative Model from a Single Natural Image], ✔️ [Real or Not Real, that is the Question], ✔️ [Training End-to-end Single Image Generators without GANs], ✔️ [DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation], ✔️ [Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks], ✔️ [GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks], ✔️ [MGGR: MultiModal-Guided Gaze Redirection with Coarse-to-Fine Learning], ✔️ [Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild], ✔️ [AutoGAN: Neural Architecture Search for Generative Adversarial Networks], ✔️ [Animating arbitrary objects via deep motion transfer], ✔️ [First Order Motion Model for Image Animation], ✔️ [Energy-based generative adversarial network], ✔️ [Mode Regularized Generative Adversarial Networks], ✔️ [Improving Generative Adversarial Networks with Denoising Feature Matching], ✔️ [Towards Principled Methods for Training Generative Adversarial Networks], ✔️ [Unrolled Generative Adversarial Networks], ✔️ [Least Squares Generative Adversarial Networks], ✔️ [Generalization and Equilibrium in Generative Adversarial Nets], ✔️ [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium], ✔️ [Spectral Normalization for Generative Adversarial Networks], ✔️ [Which Training Methods for GANs do actually Converge], ✔️ [Self-Supervised Generative Adversarial Networks], ✔️ [Semantic Image Inpainting with Perceptual and Contextual Losses], ✔️ [Context Encoders: Feature Learning by Inpainting], ✔️ [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks], ✔️ [Globally and Locally Consistent Image Completion], ✔️ [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis], ✔️ [Eye In-Painting with Exemplar Generative Adversarial Networks], ✔️ [Generative Image Inpainting with Contextual Attention], ✔️ [Free-Form Image Inpainting with Gated Convolution], ✔️ [EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning], ✔️ [a layer-based sequential framework for scene generation with gans], ✔️ [Adversarial Training Methods for Semi-Supervised Text Classification], ✔️ [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks], ✔️ [Semi-Supervised QA with Generative Domain-Adaptive Nets], ✔️ [Good Semi-supervised Learning that Requires a Bad GAN], ✔️ [AdaGAN: Boosting Generative Models], ✔️ [GP-GAN: Towards Realistic High-Resolution Image Blending], ✔️ [Joint Discriminative and Generative Learning for Person Re-identification], ✔️ [Pose-Normalized Image Generation for Person Re-identification], ✔️ [Image super-resolution through deep learning], ✔️ [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network], ✔️ [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks], ✔️ [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild], ✔️ [Adversarial Deep Structural Networks for Mammographic Mass Segmentation], ✔️ [Semantic Segmentation using Adversarial Networks], ✔️ [Perceptual generative adversarial networks for small object detection], ✔️ [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection], ✔️ [Style aggregated network for facial landmark detection], ✔️ [Conditional Generative Adversarial Nets], ✔️ [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets], ✔️ [Conditional Image Synthesis With Auxiliary Classifier GANs], ✔️ [Deep multi-scale video prediction beyond mean square error], ✔️ [Generating Videos with Scene Dynamics], ✔️ [MoCoGAN: Decomposing Motion and Content for Video Generation], ✔️ [ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal], ✔️ [BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network], ✔️ [Connecting Generative Adversarial Networks and Actor-Critic Methods], ✔️ [C-RNN-GAN: Continuous recurrent neural networks with adversarial training], ✔️ [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient], ✔️ [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery], ✔️ [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling], ✔️ [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis], ✔️ [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions], ✔️ [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks], ✔️ [Boundary-Seeking Generative Adversarial Networks], ✔️ [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution], ✔️ [Generative OpenMax for Multi-Class Open Set Classification], ✔️ [Controllable Invariance through Adversarial Feature Learning], ✔️ [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro], ✔️ [Learning from Simulated and Unsupervised Images through Adversarial Training], ✔️ [GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification], ✔️ [1] (NIPS Goodfellow Slides)[Chinese Trans][details], ✔️ [3] [ICCV 2017 Tutorial About GANS], ✔️ [3] [A Mathematical Introduction to Generative Adversarial Nets (GAN)]. /R106 182 0 R /R7 32 0 R This is actually a neural network that incorporates data from preparation and uses current data and information to produce entirely new data. T* /R10 39 0 R /Resources << /Type /Catalog 38.35510 TL /R10 39 0 R In this paper, we present GANMEX, a novel approach applying Generative Adversarial Networks (GAN) by incorporating the to-be-explained classifier as part of the adversarial networks. /R54 102 0 R /R10 11.95520 Tf In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images. /CA 1 The generative model can be thought of as analogous to a team of counterfeiters, (Abstract) Tj >> 11.95590 TL endobj Please cite this paper if you use the code in this repository as part of a published research project. We use essential cookies to perform essential website functions, e.g. [ (2) -0.30019 ] TJ 23 Apr 2018 • Pierre-Luc Dallaire-Demers • Nathan Killoran. [ (W) 91.98650 (e) -242.00300 (e) 15.01280 (valuate) -242.01700 (LSGANs) -241.99300 (on) -241.98900 (LSUN) -242.00300 (and) -243.00400 (CIF) 115.01500 (AR\05510) -241.98400 (datasets) -242.00100 (and) ] TJ /R123 196 0 R [ (the) -261.98800 (e) 19.99240 (xperimental) -262.00300 (r) 37.01960 (esults) -262.00800 (show) -262.00500 (that) -262.01000 (the) -261.98800 (ima) 10.01300 (g) 10.00320 (es) -261.99300 (g) 10.00320 (ener) 15.01960 (ated) -261.98300 (by) ] TJ 105.25300 4.33789 Td /R50 108 0 R endstream q framework based on generative adversarial networks (GANs). >> In this paper, we propose a Distribution-induced Bidirectional Generative Adversarial Network (named D-BGAN) for graph representation learning. /R137 211 0 R Q >> [ (learning\054) -552.00500 (ho) 24.98600 (we) 25.01420 (v) 14.98280 (er) 39.98600 (\054) -551.00400 (unsupervised) -491.99800 (learni) 0.98758 (ng) -491.98700 (tasks\054) -550.98400 (such) -491.98400 (as) ] TJ /F2 97 0 R >> /R14 48 0 R In this work, … Don't forget to have a look at the supplementary as well (the Tensorflow FIDs can be found there (Table S1)). 4.02227 -3.68789 Td >> Part of Advances in Neural Information Processing Systems 29 (NIPS 2016) Bibtex » Metadata » Paper » Reviews » Supplemental » Authors. ET endobj /R16 9.96260 Tf /Resources << >> Authors: Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brebisson, Yoshua Bengio, Aaron Courville. We … >> T* /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] [ (Recently) 64.99410 (\054) -430.98400 (Generati) 24.98110 (v) 14.98280 (e) -394.99800 (adv) 14.98280 (ersarial) -396.01200 (netw) 10.00810 (orks) -395.01700 (\050GANs\051) -394.98300 (\1336\135) ] TJ /Filter /FlateDecode [ (which) -257.98100 (usually) -258.98400 (adopt) -258.01800 (approximation) -257.98100 (methods) -258.00100 (for) -259.01600 (intractable) ] TJ

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