https://github.com/Kal213/StarGAN-Tutorial-Tensorflow-2.3
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Kalyan0821/NST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kalyan0821/myelinfoundry-challenge
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KamitaniLab/DeepImageReconstruction
Generating Images with Perceptual Similarity Metrics based on Deep Networks
https://github.com/KamitaniLab/End2EndDeepImageReconstruction
Generating Images with Perceptual Similarity Metrics based on Deep Networks
https://github.com/KamitaniLab/cnnpref
Generating Images with Perceptual Similarity Metrics based on Deep Networks
https://github.com/KamitaniLab/icnn
Generating Images with Perceptual Similarity Metrics based on Deep Networks
https://github.com/KanikaNegi/text-to-image-using-GAN
https://github.com/KarT1994/EmnistRecognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KarimaCha/CNN_Trust_Prediction
https://github.com/Karine-Huang/T2I-CompBench
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
https://github.com/KarolyPoka/ULTRON
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/Kate589/gan
https://github.com/KatherineBaq/NMA-CN2021-fMRI_vs_DNNs
https://github.com/KbeautyHair/BaselineModel
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/KeiraZhao/MDAN
https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10
https://github.com/KellyHwong/rethinking_generalization
https://github.com/KiUngSong/Generative-Models
https://github.com/King-HAW/CT_inpainting
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/King-Otaku/Emotion_Recognition_DNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kinyugo/consistency_models
https://github.com/KishanKancharagunta/CDGAN
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation
https://github.com/Kokonut133/MagicDraw
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/Kokonut133/frame2frame
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/KongBOy/analyze_16_code_iin
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
https://github.com/KonstantinPakulev/OnGansAndGMMs
https://github.com/KookHoiKim/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/KostasTok/keras_DCGAN_transfer_learning
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Kprerak-iisc/data-science
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/KrishnaswamyLab/travelgan
TraVeLGAN: Image-to-image Translation by Transformation Vector Learning
https://github.com/Ksuryateja/DCGAN-CIFAR10-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Ksuryateja/DCGAN-MNIST-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/KumapowerLIU/DeFLOCNet
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/KunStats/Paddle-DIP
https://github.com/KwonGihyun/DiagonalGAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.