Cross-Domain Few-Shot

12 papers with code • 1 benchmarks • 1 datasets

This task has no description! Would you like to contribute one?

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.

Classification cross-domain few-shot learning +3

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

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

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.

Classification Cross-Domain Few-Shot +3

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 Classification with Feature Map Reconstruction Networks

Tsingularity/FRN 2 Dec 2020

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

Classification Cross-Domain Few-Shot +1

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.

Classification Cross-Domain Few-Shot +4

Modular Adaptation for Cross-Domain Few-Shot Learning

frkl/modular-adaptation 1 Apr 2021

Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples.

cross-domain few-shot learning Representation Learning