Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and
execute tasks with human cognitive abilities, engenders significant anticipation and intrigue …

Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from
input data through a model. This problem has attracted increasing attention in the area of …

Domain generalization (DG) aims at solving distribution shift problems in various scenes.
Existing approaches are based on Convolution Neural Networks (CNNs) or Vision …

Deep learning methods can struggle to handle domain shifts not seen in training data, which
can cause them to not generalize well to unseen domains. This has led to research attention …

Ideally, visual learning algorithms should be generalizable, for dealing with any unseen
domain shift when deployed in a new target environment; and data-efficient, for reducing …

Abstract Out-of-Distribution (OOD) Generalization aims to learn robust models that
generalize well to various environments without fitting to distribution-specific features …

Deep neural network has demonstrated high-level accuracy in rail surface defect
segmentation. However, deploying these deep models in actual inspection situations results …

Domain generalization (DG) aims to tackle the distribution shift between training domains
and unknown target domains. Generating new domains is one of the most effective …

Abstract Domain Generalization is a challenging problem in deep learning especially in
medical image analysis because of the huge diversity between different datasets. Existing …