https://github.com/lonlyness/pggan-structure
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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/lonlyness/variational_temporal_abstraction
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/lonnyzhang423/github-hot-hub
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lorenzo-stacchio/Stable-Diffusion-Inpaint
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lornatang/cyclegan-pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/lornatang/googlenet-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/louaaron/scaling-riemannian-diffusion
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
https://github.com/ls-da3m0ns/Short-ResNet
https://github.com/lsh110600/person_re_id
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/lshaw8317/mlmcfordms
https://github.com/lucasbotang/modulargan
https://github.com/lucasgb98/StyleGAN2
https://github.com/lucashueda/long_sentence_transformer
https://github.com/lucastheis/isa
https://github.com/lucataco/cog-differential-diffusion
https://github.com/luchengthu/mle_score_ode
https://github.com/lucidrains/DALLE-pytorch
https://github.com/lucidrains/DALLE2-pytorch
https://github.com/lucidrains/agent-attention-pytorch
Agent Attention: On the Integration of Softmax and Linear Attention
https://github.com/lucidrains/axial-attention
https://github.com/lucidrains/big-sleep
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/lucidrains/bit-diffusion
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
https://github.com/lucidrains/chroma-pytorch
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
https://github.com/lucidrains/classifier-free-guidance-pytorch
https://github.com/lucidrains/ddpm-proteins
https://github.com/lucidrains/denoising-diffusion-pytorch
https://github.com/lucidrains/ema-pytorch
Analyzing and Improving the Training Dynamics of Diffusion Models
https://github.com/lucidrains/gigagan-pytorch
https://github.com/lucidrains/hourglass-transformer-pytorch
Hierarchical Transformers Are More Efficient Language Models
https://github.com/lucidrains/lightweight-gan
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
https://github.com/lucidrains/local-attention
Efficient Content-Based Sparse Attention with Routing Transformers
https://github.com/lucidrains/magvit2-pytorch
https://github.com/lucidrains/make-a-video-pytorch
https://github.com/lucidrains/muse-pytorch
Muse: Text-To-Image Generation via Masked Generative Transformers
https://github.com/lucidrains/nuwa-pytorch
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
https://github.com/lucidrains/parti-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.