Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich
source domain to an unlabeled target domain without seeing source data. While most …

Cross-domain (CD) hyperspectral image classification (HSIC) has been significantly
boosted by methods employing Few-Shot Learning (FSL) based on CNNs or GCNs …

Deep learning has been highly successful in computer vision with large amounts of labeled
data, but struggles with limited labeled training data. To address this, Few-shot learning …

Cross-domain few-shot Learning (CDFSL) is proposed to first pre-train deep models on a
source domain dataset where sufficient data is available, and then generalize models to …

Few-shot learning aims to recognize novel concepts with only few samples by using prior
knowledge learned from the seen concepts. In this paper, we address the problem of few …

Due to the limited availability of labelled data in many real-world scenarios, we have to
resort to data from other domains to improve models' performance, which prompts the …

Robustness to natural distribution shifts has seen remarkable progress thanks to recent pre-
training strategies combined with better fine-tuning methods. However, such fine-tuning …