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Found 5 papers, 1 papers with code
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning
For this reason, this paper explores a Source-Free CDFSL (SF-CDFSL) problem, in which CDFSL is addressed through the use of existing pretrained models instead of training a model with source data, avoiding accessing source data.
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with limited labeled training data.
Cross-Domain Few-Shot Classification via Inter-Source Stylization
The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets.
An Edge Information and Mask Shrinking Based Image Inpainting Approach
The mask shrinking strategy is employed in the image completion model to track the areas to be repaired.
SNR-Based Teachers-Student Technique for Speech Enhancement
Specifically, this method consists of multiple teacher models and a student model.
