cross-domain few-shot learning

7 papers with code • 0 benchmarks • 0 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.

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

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

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

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

Open-Debin/MetaQDA_Pub 8 Jan 2021

Current state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple, e. g. nearest centroid, classifiers.

cross-domain few-shot learning