Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 23
- 평가표
- 93
- 보존된 평가 기록
- 870
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/Kyfafyd/MirrorGAN
MirrorGAN: Learning Text-to-image Generation by Redescription
https://github.com/L0SG/NanoFlow
https://github.com/LAION-AI/phenaki
https://github.com/LEChaney/AudioStyleGAN
https://github.com/LEEPEIQIN/EDSR
https://github.com/LEGO999/BIgBiGAN
https://github.com/LEGO999/BigBiGAN-TensorFlow2.0
https://github.com/LJSthu/info-GAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/LKLQQ/StarGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/LKLQQ/ssc_resnet50
https://github.com/LLaVA-VL/LLaVA-NeXT
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
https://github.com/LONG-9621/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/LWChen20/RCAN
https://github.com/LYMDLUT/improved_consistency_models_cifar10_pytorch
https://github.com/Labyz/SinGAN-MVA
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/LanceZPF/OpenING
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
https://github.com/LansburyCH/relightable-nr
https://github.com/LarryJiang134/Image_manipulation_detection
https://github.com/Latope2-150/Progressive_Growing_of_GANs-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/LeChangAlex/GAN-Metric
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Leinadh/avatar-image-generator
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/Leminhbinh0209/AAAI22-ADD
Deferred Neural Rendering: Image Synthesis using Neural Textures
https://github.com/Leminhbinh0209/GANFingerprints-pytorch
Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints
https://github.com/LemonTwoL/ReNeg
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
https://github.com/Lester1711/DSI15_Capstone
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LiYinqi/DIVE
https://github.com/LiYuhangUSTC/Sketch2Face
https://github.com/LiangHann/USAR
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
https://github.com/LightNormal/StyleGAN2withGUI
https://github.com/LignumResearch/stylewood-model-usage
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/Lillian1082/IRAE_pytorch
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
https://github.com/LingaswamyD23/GAN_Using_Pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Little-Frog-233/imagecluster
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LiuHuijie6410/OmniDish
Omni-Dish: Photorealistic and Faithful Image Generation and Editing for Arbitrary Chinese Dishes
https://github.com/LiuNull/pix2pix_Liu
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.