8-shot Accuracy: 82.64-shot Accuracy: 66.712-shot Accuracy: 88.816-shot Accuracy: 91.0
Papers with Code · CC BY-SA 4.0
Few-Shot Learning
Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.
<span class="description-source">Source: Penalty Method for Inversion-Free Deep Bilevel Optimization </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
69개논문
Harmonic mean: 73.07
8-shot Accuracy: 71.84-shot Accuracy: 42.212-shot Accuracy: 82.916-shot Accuracy: 88.9
Acc: 93.5(0.5)
8-shot Accuracy: 73.14-shot Accuracy: 44.312-shot Accuracy: 83.916-shot Accuracy: 88.3
Accuracy: 77.9
Accuracy: 73.42
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.
하위 태스크
FS
Few-Shot Semantic Segmentation
CF
Cross-Domain Few-Shot
FA
Few-Shot Audio Classification
FR
Few-Shot Relation Classification
OL
One-Shot Learning
FC
Few-Shot Camera-Adaptive Color Constancy
FI
Few-Shot Imitation Learning
FV
Few-Shot Video Object Detection
FH
Few-shot HTC
UF
Unsupervised Few-Shot Audio Classification
UF
Unsupervised Few-Shot Learning
F
few-shot-htc