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/ewrfcas/pgic_inpainting
공식 코드https://github.com/ewrfcas/zits-plusplus
공식 코드ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors
https://github.com/explainableml/datadream
공식 코드https://github.com/explainableml/delora
공식 코드DeLoRA: Decoupling Angles and Strength in Low-rank Adaptation
https://github.com/explainableml/imageselect
공식 코드https://github.com/explainableml/loft
공식 코드LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance
https://github.com/explainableml/uncertaintyawarecycleconsistency
공식 코드https://github.com/exponentiai/haifit
공식 코드https://github.com/exponentialml/text-to-video-finetuning
공식 코드https://github.com/exx8/differential-diffusion
공식 코드https://github.com/ezioby/glead
공식 코드https://github.com/ezioby/padinv
공식 코드https://github.com/f90/FactorGAN
공식 코드https://github.com/fabianaltekrueger/conditional_mmd_flows
공식 코드Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
https://github.com/fabiotosi92/monoResMatch-Tensorflow
공식 코드Learning monocular depth estimation infusing traditional stereo knowledge
https://github.com/face3d0725/face_de_mask
공식 코드https://github.com/facebookresearch/DiT
공식 코드https://github.com/facebookresearch/StyleNeRF
공식 코드StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis
https://github.com/facebookresearch/dig-in
공식 코드DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
https://github.com/facebookresearch/ic_gan
공식 코드https://github.com/facebookresearch/irmae
공식 코드https://github.com/facebookresearch/metaclip
공식 코드https://github.com/facebookresearch/mils
공식 코드https://github.com/facebookresearch/pytorch_GAN_zoo
공식 코드https://github.com/facebookresearch/rcdm
공식 코드High Fidelity Visualization of What Your Self-Supervised Representation Knows About
https://github.com/facebookresearch/viewdiff
공식 코드ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models
https://github.com/facebookresearch/vsait
공식 코드Unpaired Image Translation via Vector Symbolic Architectures
https://github.com/facebookresearch/wmar
공식 코드https://github.com/faceeyes/evogan
공식 코드https://github.com/fahimzaman/ldseg
공식 코드Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracy
https://github.com/fakerone-li/skipvar
공식 코드SkipVAR: Accelerating Visual Autoregressive Modeling via Adaptive Frequency-Aware Skipping
https://github.com/fallenshock/SinDDM
공식 코드https://github.com/fallenshock/Slicedit
공식 코드Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices
https://github.com/fangyuanmao/pid
공식 코드PID: Physics-Informed Diffusion Model for Infrared Image Generation
https://github.com/faniamokhayeri/C-GAN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.