FID-5k-training-steps: 9.1679
Papers with Code · CC BY-SA 4.0
Image Generation
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>
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.
Dataset기록된 모델 예시MetricPaperCode
데이터셋
249개논문
FID-5k-training-steps: 0.89
FID: 11.25Inception score: 52.53
FID: 10.3
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
FID: 13.20Inception score: 47.32
FID: 34.91Inception score: 13.50
FID: 67.41
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.
하위 태스크
IT
Image-to-Image Translation
TG
Text-to-Image Generation
CI
Conditional Image Generation
II
Image Inpainting
LG
Layout-to-Image Generation
FG
Face Generation
IH
Image Harmonization
PT
Pose Transfer
3I
3D-Aware Image Synthesis
IG
Image Generation from Scene Graphs
3G
3D Generation
IM
Image Manipulation