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Unsupervised Few-Shot Learning

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In contrast to supervised few-shot learning, only the unlabeled dataset is available in the pre-training or meta-training stage for unsupervised few-shot learning.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AutodeskAILab/UVStyle-Net

공식 코드

UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps

https://github.com/WonderSeven/ULDA

공식 코드

Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

https://github.com/anish-lu-yihe/pySVRT

공식 코드

Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning Test

https://github.com/bbbdylan/unisiam

공식 코드

Self-Supervision Can Be a Good Few-Shot Learner

https://github.com/hanlu-nju/revisiting-uml

공식 코드

Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks

https://github.com/icgy96/micm

공식 코드

MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning

https://github.com/indy-lab/ProtoTransfer

공식 코드

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

https://github.com/ojss/samptransfer

공식 코드

Self-Attention Message Passing for Contrastive Few-Shot Learning

https://github.com/stypoumic/beclr

공식 코드

BECLR: Batch Enhanced Contrastive Few-Shot Learning

https://github.com/wenbinlee/trip-roma

공식 코드

Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.