> /R10 10.16190 Tf /a0 << >> /x24 21 0 R /Rotate 0 In this work, … [Generative Adversarial Networks, Ian J. Goodfellow et al., NIPS 2016]에 대한 리뷰 영상입니다. Generative Adversarial Nets. >> GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Two novel losses suitable for cartoonization are pro-posed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of the VGG network … /ExtGState << stream 270 32 72 14 re /x15 18 0 R >> q [ <636c6173736902636174696f6e> -630.00400 (\1337\135\054) -331.98300 (object) -314.99000 (detection) -629.98900 (\13327\135) -315.98400 (and) -315.00100 (se) 15.01960 (gmentation) ] TJ /R18 59 0 R Instead of the widely used normal distribution assumption, the prior dis- tribution of latent representation in our DBGAN is estimat-ed in a structure-aware way, which implicitly bridges the graph and feature spaces by prototype learning. /I true << /Length 17364 /ExtGState << We present Time-series Generative Adversarial Networks (TimeGAN), a natural framework for generating realistic time-series data in various domains. Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices. /I true T* endobj We propose a novel framework for generating realistic time-series data that combines … 38.35510 TL [ (\1338\135\054) -315.00500 (DBM) -603.99000 (\13328\135) -301.98500 (and) -301.98300 (V) 135 (AE) -604.01000 (\13314\135\054) -315 (ha) 19.99790 (v) 14.98280 (e) -303.01300 (been) -301.98600 (proposed\054) -315.01900 (these) ] TJ [ (which) -265 (adopt) -264.99700 (the) -265.00700 (least) -263.98300 (squares) -265.00500 (loss) -264.99000 (function) -264.99000 (for) -265.01500 (the) -265.00500 (discrim\055) ] TJ >> /R10 10.16190 Tf /R135 209 0 R Learn more. [ (vised) -316.00600 (learning) -316.98900 (tasks\056) -508.99100 (Unl) 0.99493 (ik) 10.00810 (e) -317.01100 (other) -316.01600 (deep) -315.98600 (generati) 24.98600 (v) 14.98280 (e) -317.01100 (models) ] TJ /Rotate 0 [ (Least) -223.99400 (Squares) -223.00200 (Generati) 24.98110 (v) 14.98280 (e) -224.00700 (Adv) 14.99260 (ersarial) -224.00200 (Netw) 10.00810 (orks) -223.98700 (\050LSGANs\051) ] TJ /x12 20 0 R [ (ha) 19.99670 (v) 14.98280 (e) -359.98400 (sho) 24.99340 (wn) -360.01100 (that) -360.00400 (GANs) -360.00400 (can) -359.98400 (play) -360.00400 (a) -361.00300 (si) 0.99493 <676e690263616e74> -361.00300 (role) -360.01300 (in) -360.00900 (v) 24.98110 (ar) 19.98690 (\055) ] TJ Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. -11.95510 -11.95470 Td Given a training set, this technique learns to generate new data with the same statistics as the training set. /F2 43 0 R Work fast with our official CLI. /Type /XObject [ (CodeHatch) -250.00200 (Corp\056) ] TJ /Font << [ (2) -0.30019 ] TJ /R12 7.97010 Tf q Two novel losses suitable for cartoonization are pro-posed: (1) a semantic content loss, which is formulated as [ (ha) 19.99670 (v) 14.98280 (e) -496 (demonstrated) -497.01800 (impressi) 25.01050 (v) 14.98280 (e) -496 (performance) -495.99600 (for) -497.01500 (unsuper) 20.01630 (\055) ] TJ /ExtGState << << /R56 105 0 R /R79 123 0 R /R7 32 0 R /Type /Pages [ (Department) -249.99300 (of) -250.01200 (Information) -250 (Systems\054) -250.01400 (City) -250.01400 (Uni) 25.01490 (v) 15.00120 (ersity) -250.00500 (of) -250.01200 (Hong) -250.00500 (K) 35 (ong) ] TJ >> /R7 32 0 R /Filter /FlateDecode We use essential cookies to perform essential website functions, e.g. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data … /ExtGState << /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] 11.95510 -17.51720 Td T* /Subtype /Form << /R52 111 0 R 11.95510 -19.75900 Td /R42 86 0 R /Group << 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. /XObject << 11.95510 TL /R97 165 0 R they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. data synthesis using generative adversarial networks (GAN) and proposed various algorithms. stream [ (stability) -249.98900 (of) -249.98500 (LSGANs\056) ] TJ /ca 1 /s5 33 0 R /Contents 185 0 R 4 0 obj ArXiv 2014. In this paper, we introduce two novel mechanisms to address above mentioned problems. 11.95470 TL /R20 63 0 R /F1 12 Tf 2 0 obj /R137 211 0 R Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. >> endobj /R10 10.16190 Tf x�e�� AC����̬wʠ� ��=p���,?��]%���+H-lo�䮬�9L��C>�J��c���� ��"82w�8V�Sn�GW;�" /R42 86 0 R 11.95590 TL >> << Q 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 … /R69 175 0 R /R7 32 0 R [ (tor) -269.98400 (aims) -270.01100 (to) -271.00100 (distinguish) -270.00600 (between) -269.98900 (real) -270 (samples) -270.00400 (and) -271.00900 (generated) ] TJ /R75 168 0 R /R18 59 0 R T* /x8 14 0 R /Type /XObject /R10 39 0 R /R73 127 0 R [ (lem) -261.01000 (during) -260.98200 (the) -261.00800 (learning) -262 (pr) 44.98390 (ocess\056) -342.99100 (T) 92 (o) -261.01000 (o) 10.00320 (ver) 37.01100 (come) -261.01500 (suc) 14.98520 (h) -261.99100 (a) -261.01000 (pr) 44.98510 (ob\055) ] TJ /R10 39 0 R >> 80.85700 0 Td [ (tiable) -336.00500 (netw) 10.00810 (orks\056) -568.00800 (The) -334.99800 (basic) -336.01300 (idea) -336.01700 (of) -335.98300 (GANs) -336.00800 (is) -336.00800 (to) -336.01300 (simultane\055) ] TJ ET To bridge the gaps, we conduct so far the most comprehensive experimental study that investigates apply-ing GAN to relational data synthesis. /ExtGState << T* /R10 39 0 R GANs, first introduced by Goodfellow et al. /Type /Page /a0 gs /R113 186 0 R The method was developed by Ian Goodfellow in 2014 and is outlined in the paper Generative Adversarial Networks.The goal of a GAN is to train a discriminator to be able to distinguish between real and fake data … 11.95590 TL [ (Deep) -273.01400 (learning) -272.01600 (has) -273.00600 (launched) -272.99900 (a) -271.99900 (profound) -272.98900 (reformation) -272.99100 (and) ] TJ 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. /s11 gs The network learns to generate faces from voices by matching the identities of generated faces to those of the speakers, on a training set. Generative Adversarial Imitation Learning. /F2 134 0 R T* In this paper, we introduce two novel mechanisms to address above mentioned problems. >> /R20 63 0 R [ (\13318\135\056) -297.00300 (These) -211.99800 (tasks) -211.98400 (ob) 14.98770 (viously) -212.00300 (f) 9.99466 (all) -211.01400 (into) -212.01900 (the) -211.99600 (scope) -211.99600 (of) -212.00100 (supervised) ] TJ T* >> /R40 90 0 R x�+��O4PH/VЯ0�Pp�� /x18 15 0 R The code allows the users to reproduce and extend the results reported in the study. 11.95590 TL endstream /R62 118 0 R /F2 226 0 R Learn more. /R14 48 0 R /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R ] /Resources 16 0 R [ (of) -292.01700 (LSGANs) -291.98400 (o) 10.00320 (ver) -291.99300 (r) 37.01960 (e) 39.98840 (gular) -290.98200 (GANs\056) -436.01700 (F) 45.01580 (ir) 10.01180 (st\054) -302.01200 (LSGANs) -291.98300 (ar) 36.98650 (e) -291.99500 (able) -292.01700 (to) ] TJ /MediaBox [ 0 0 612 792 ] /Rotate 0 /R12 6.77458 Tf Q q We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. /R8 55 0 R 1 1 1 rg Paper where method was first introduced: ... Quantum generative adversarial networks. f /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] /Resources << endstream In this paper, we propose Car-toonGAN, a generative adversarial network (GAN) frame-work for cartoon stylization. /Resources << /R16 51 0 R /R58 98 0 R /a0 << endobj /Resources << Awesome papers about Generative Adversarial Networks. Unlike the CNN-based methods, FV-GAN learns from the joint distribution of finger vein images and … T* /F2 183 0 R /R81 148 0 R stream /Rotate 0 >> The code allows the users to reproduce and extend the results reported in the study. In this paper, we propose a principled GAN framework for full-resolution image compression and use it to realize 1221. an extreme image compression system, targeting bitrates below 0.1bpp. x�+��O4PH/VЯ02Qp�� >> /Resources << Please help contribute this list by contacting [Me][zhang163220@gmail.com] 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] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details], ✔️ [3] [ICCV 2017 Tutorial About GANS], ✔️ [3] [A Mathematical Introduction to Generative Adversarial Nets (GAN)].

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