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/hsinyilin19/resnetvae
https://github.com/hssip/fashionsap
https://github.com/hthoai/UI2IT
https://github.com/huage001/linfusion
CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up
https://github.com/huangzh13/StyleGAN.pytorch
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/huangzh13/StyleGAN2.pytorch
https://github.com/huanngzh/MV-Adapter
MV-Adapter: Multi-view Consistent Image Generation Made Easy
https://github.com/huawei-noah/Pruning
https://github.com/hubert0527/COCO-GAN
https://github.com/huchen365/ds
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
https://github.com/huggingface/block_movement_pruning
Training with Quantization Noise for Extreme Model Compression
https://github.com/huggingface/open-muse
Muse: Text-To-Image Generation via Masked Generative Transformers
https://github.com/huggingface/transformers
https://github.com/hughplay/DFNet
https://github.com/huihuangz/Neural-Style-Transfer-Papers-Code
Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft Masks
https://github.com/huijieZH/Diffusion-Model-Generalizability
The Emergence of Reproducibility and Generalizability in Diffusion Models
https://github.com/hujinsen/pytorch-StarGAN-VC
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/hukangli/https-github.com-deepinsight-insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/hukkelas/deep_privacy2
Self-Distilled StyleGAN: Towards Generation from Internet Photos
https://github.com/hukkelas/progan-pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/hukkelas/pytorch-frechet-inception-distance
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/human-analysis/RankGAN
https://github.com/humansensinglab/cycle-diffusion
Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance
https://github.com/hungdhv97/Glow-Generative-Flow-with-Invertible-1x1-Convolutions
https://github.com/hungtrankhanh/CS5260_project
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
https://github.com/hunnurjirao/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/husnain-ali21/Image_Generation_using_StyleGAN
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
https://github.com/hussain18804/toonify-poc
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/PixelHacker
PixelHacker: Image Inpainting with Structural and Semantic Consistency
https://github.com/hustvl/dig
https://github.com/hustvl/sparseinst
https://github.com/huybik/GAN-Experiments-Pytorch
https://github.com/huybik/GANs-Pytorch
https://github.com/huynhhoanghuy/Image-Inpainting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.