https://github.com/alexandonian/ganocracy
GAN Dissection: Visualizing and Understanding 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/alexbrx/stargan
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/alexdominguez09/pedestrian_direction
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/alexeyhorkin/ProGAN-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/alexjc/neural-doodle
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/alexmotogna/attngan
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/alexmotogna/generatorapi
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/alexquach/cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/alexsax/midlevel-reps
https://github.com/alexverine/precisionrecallbiggan
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/alexwdong/IncubatorCVProject
https://github.com/alfagao/DeOldify
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/alhasan-abdellatif/cgans
https://github.com/ali-vilab/FlashFace
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
https://github.com/ali-vilab/cones-v2
Cones 2: Customizable Image Synthesis with Multiple Subjects
https://github.com/ali-vilab/i2vgen-xl
Composer: Creative and Controllable Image Synthesis with Composable Conditions
https://github.com/ali-vilab/videocomposer
Composer: Creative and Controllable Image Synthesis with Composable Conditions
https://github.com/aliaksandrsiarohin/mem-transfer
How to Make an Image More Memorable? A Deep Style Transfer Approach
https://github.com/alibaba/EasyCV
https://github.com/alibaba/EasyNLP/tree/master/examples/text2image_generation
https://github.com/alibaba/graph-gpt
https://github.com/aliciafmachado/artgan-implementation
https://github.com/alililia/ascend_FaceAttribute
https://github.com/alililia/ascend_SE-Net
https://github.com/alililia/ascend_U-GAT-IT
https://github.com/alililia/ms_contrib/tree/main/ascend_vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alililia/ms_contrib/tree/main/gpu_vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alililia/ms_extend/tree/main/gpu_pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/alililia/ms_extend/tree/main/gpu_resnet
https://github.com/alililia/vision/tree/main/CycleGAN-MS
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/alimohammadiamirhossein/cora
Cora: Correspondence-aware image editing using few step diffusion
https://github.com/alinstein/Modify-image-by-text
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/alisha17/benchmarks
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/allenai/ai2thor-rearrangement
https://github.com/allenzren/open-pi-zero
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
https://github.com/alrojo/lasagne_residual_network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.