https://github.com/DarkGeekMS/Tensorflow-GANs-Architectures-Implementation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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/Darth-Kronos/Unsupervised-Domain-Adaptation
https://github.com/Data-drone/cvnd_image_captioning
https://github.com/DataScienceNigeria/Fake-Detection-dataset-for-deepfake-from-Google-and-Jigsaw
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/DataSeeds-ai/DSD-finetune-blip-llava
https://github.com/DavidCastilloAlvarado/U-NET_AUTOENCODER
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Dawir7/SupResDiffGAN
https://github.com/DeVyacheslav/PyTorch-MNIST-DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Deci-AI/super-gradients
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DeepK/inpainting
Region-of-interest guided Supervoxel Inpainting for Self-supervision
https://github.com/DeepMIALab/AI-FFPE
Contrastive Learning for Unpaired Image-to-Image Translation
https://github.com/DeepMark/deepmark
https://github.com/DeepikaKaranji/CycleGAN-Unpaired-Image-translation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Demonhesusheng/Reid
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/Deopoler/DCLGAN-Keras
Dual Contrastive Learning for Unsupervised Image-to-Image Translation
https://github.com/DevashishJoshi/Transferring-GANs-FYP
https://github.com/Di-Is/stylegan2-ada-pytorch
https://github.com/DingDing33/BPS-v1-1
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/Discover304/SinGlow
https://github.com/DivJAth/DeepLearning5922
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Djmcflush/Quantum-Hackathon
https://github.com/DmitryUlyanov/deep-image-prior
https://github.com/DmitryUlyanov/fast-neural-doodle
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/Dominioncher/smart-sketch
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/DosImpact/focusonme_core
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/Dou-Yu-xuan/FSRNet
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
https://github.com/DrRyanHuang/VGGNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DreamTechAI/Direct3D
Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer
https://github.com/DreamTechAI/Direct3D-S2
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
https://github.com/Duplums/bhb10k-dl-benchmark
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/E-DEEP/PapersReview
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EiroaMD/final_project_IH_pneumonia
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Eitan177/testGenImages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.