Cross-Domain Few-Shot

15 papers with code • 1 benchmarks • 1 datasets

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Greatest papers with code

Self-Supervised Learning For Few-Shot Image Classification

phecy/SSL-FEW-SHOT 14 Nov 2019

In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.

cross-domain few-shot learning Few-Shot Image Classification +2

A Broader Study of Cross-Domain Few-Shot Learning

IBM/cdfsl-benchmark ECCV 2020

Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.

cross-domain few-shot learning Few-Shot Image Classification +1

Cross-Domain Few-Shot Learning with Meta Fine-Tuning

johncai117/Meta-Fine-Tuning 21 May 2020

In our final results, we combine the novel method with the baseline method in a simple ensemble, and achieve an average accuracy of 73. 78% on the benchmark.

cross-domain few-shot learning Data Augmentation +2

Few-Shot Classification with Feature Map Reconstruction Networks

Tsingularity/FRN CVPR 2021

In this paper we reformulate few-shot classification as a reconstruction problem in latent space.

Cross-Domain Few-Shot General Classification

Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

Haoqing-Wang/CDFSL-ATA 29 Apr 2021

However, when there exists the domain shift between the training tasks and the test tasks, the obtained inductive bias fails to generalize across domains, which degrades the performance of the meta-learning models.

Cross-Domain Few-Shot Domain Generalization +2

Cross-Domain Few-Shot Learning by Representation Fusion

ml-jku/chef 13 Oct 2020

On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.

cross-domain few-shot learning Drug Discovery

Improving Task Adaptation for Cross-domain Few-shot Learning

VICO-UoE/URL 1 Jul 2021

In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples.

cross-domain few-shot learning

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

leezhp1994/TMHFS 8 Jun 2020

The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.

cross-domain few-shot learning Data Augmentation

Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier

arjish/PreTrainedFullLibrary_FewShot 3 Jan 2021

Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification.

Cross-Domain Few-Shot Few-Shot Image Classification +3