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Cross-Domain Few-Shot

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평가표

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보존된 평가 기록

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코드

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

https://github.com/zoilsen/flor

공식 코드

Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning

https://github.com/zouxinyi0625/cdnet

공식 코드

Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition

https://github.com/zshanggu/cdmm

공식 코드

Cross-domain Multi-modal Few-shot Object Detection via Rich Text

https://github.com/zuwenqiang/ept

공식 코드

Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical Images

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/unisiam

Self-Supervision Can Be a Good Few-Shot Learner

https://github.com/Dipeshtamboli/Cross-Domain-FSL-via-NSAE

Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder

https://github.com/HoganZhang/few-shot-gnn

Few-Shot Learning with Graph Neural Networks

https://github.com/IBM/cdfsl-benchmark

A Broader Study of Cross-Domain Few-Shot Learning

https://github.com/Lieberk/Paddle-FSL-GNN

Few-Shot Learning with Graph Neural Networks

https://github.com/MindSpore-paper-code-2/code3/tree/main/unisiam

Self-Supervision Can Be a Good Few-Shot Learner

https://github.com/Open-Debin/MetaQDA_Pub

Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

https://github.com/hushell/pmf_cvpr22

A Broader Study of Cross-Domain Few-Shot Learning

https://github.com/jimzai/deta

Cross-domain Few-shot Learning with Task-specific Adapters

https://github.com/leesb7426/cvpr2022-task-discrepancy-maximization-for-fine-grained-few-shot-classification

Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification

https://github.com/leezhp1994/TMHFS

A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning

https://github.com/louis2889184/gnn_few_shot_cifar100

Few-Shot Learning with Graph Neural Networks

https://github.com/nobody-1617/deta

Cross-domain Few-shot Learning with Task-specific Adapters

https://github.com/peymanbateni/simple-cnaps

Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

https://github.com/phecy/SSL-FEW-SHOT

Self-Supervised Learning For Few-Shot Image Classification

https://github.com/sungnyun/cd-fsl

Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty

https://github.com/suvarna-kadam/Oct2018_Demo

Few-Shot Learning with Graph Neural Networks

https://github.com/xxxnhb/few-shot-gnn

Few-Shot Learning with Graph Neural Networks

https://github.com/yangyucheng000/MSpaper/tree/main/unisiam

Self-Supervision Can Be a Good Few-Shot Learner

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