Mar 1, 2024 · Abstract:Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited training data in the target domain by leveraging ...

Feb 1, 2022 · Abstract:Cross-domain few-shot learning (CD-FSL) has drawn increasing attention for handling large differences between the source and target ...

Its essence is transfer learning. The model needs to be trained in the source domain and then migrated to the target domain. Compliant with (1) the category ...

Recently,. [47] proposes a successful meta-learning approach based on conditional neural process on the MetaDataset benchmark. Cross-domain Few-shot Learning In ...

In this paper, we empirically investigate which pre-training is preferred based on domain similarity and few-shot difficulty of the target domain. We discover ...

This data enables self-supervised pre-training on the target domain, in addition to supervised pre-training on the source domain. In this paper, we empirically ...

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 ...

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input ...

Few-shot learning aims at learning from few examples, often by using already acquired knowledge and, therefore, enabling models to quickly adapt to new data ...

Few-shot classification aims to recognize unseen classes with few labeled samples from each class. Many meta-learning models for few-shot classifica-.