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/ex4sperans/variational-inference-with-normalizing-flows
https://github.com/explainingai-code/ControlNet-PyTorch
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/explainingai-code/DDPM-Pytorch
https://github.com/explainingai-code/Dalle-Pytorch
https://github.com/explainingai-code/StableDiffusion-PyTorch
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/eyalbetzalel/GLOW
https://github.com/eyalbetzalel/GLOW2
https://github.com/eyalbetzalel/pytorch-generative
https://github.com/eyalbetzalel/pytorch-generative-v2
https://github.com/eyalbetzalel/pytorch-generative-v6
https://github.com/eyalbetzalel/residual-flows
https://github.com/ezelikman/STaR
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
https://github.com/fL0n9/SKFAC-MindSpore
https://github.com/fabioasdias/ubao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fabiofumarola/ultrayolo
https://github.com/fabxy/accessibilityCV
https://github.com/facebook/fb.resnet.torch
https://github.com/facebookarchive/fb.resnet.torch
https://github.com/facebookresearch/DomainBed
https://github.com/facebookresearch/chameleon
https://github.com/facebookresearch/detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/facebookresearch/evalgim
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
https://github.com/facebookresearch/fairscale
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
https://github.com/facebookresearch/flowmm
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
https://github.com/facebookresearch/hiera
https://github.com/facebookresearch/msn
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
https://github.com/facebookresearch/multimodal
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/facebookresearch/pycls
https://github.com/facebookresearch/semanticimagetranslation
https://github.com/facebookresearch/stochastic_gradient_push
https://github.com/facebookresearch/vissl
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
https://github.com/facebookresearch/w2ot
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
https://github.com/facebookresearch/xformers
https://github.com/factoryIO/1-simple_neural_style_transfer
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/facundoq/transformational_measures_experiments
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.