https://github.com/kynk94/TF2-Image-Generation
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/kyranstar/Narcissus
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/kzhou23/shape_pose_disent
https://github.com/kzkadc/poly-nets
https://github.com/kzkymur/transition
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/lab-midas/med_segmentation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/laclark/cuneiform_lineart
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/lagergren-lab/miselbo
https://github.com/laion-ai/conditioned-prior
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/lancelee82/necklace
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
https://github.com/lancerane/Adversarial-domain-adaptation
https://github.com/landeros10/infoganJL
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/laowng/GISR
https://github.com/laplaisanterie/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/larsmaaloee/BIVA
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
https://github.com/larsoncs/face_detect
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/lastmile-ai/aiconfig
https://github.com/laurent-dinh/nice
https://github.com/lavender105/rsgpt
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
https://github.com/lavolpiana/deep-image-prior
https://github.com/layer6ai-labs/dgm_manifold_survey
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
https://github.com/layer6ai-labs/direct-cms
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
https://github.com/layer6ai-labs/fusemix
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation
https://github.com/layoutllm-t2i/layoutllm-t2i
LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation
https://github.com/layumi/DG-Net
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/layumi/Person_reID_baseline_pytorch
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/leebebeto/pytorch-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leehomyc/Faster-High-Res-Neural-Inpainting
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
https://github.com/leehomyc/cyclegan-1
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/leejaeka/PixelGAN
https://github.com/leejaeka/incrementum
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/leenaali1114/Hierarchical-Image-Clustering---Unsupervised-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leffff/FlowModels
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.