Domain Generalization through Meta-Learning: A Survey

arXiv:2404.02785v1 Announce Type: new
Abstract: Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications. This limitation stems from the common assumption that training and testing data share the same distribution-an assumption frequently violated in practice. Despite their effectiveness with large amounts of data and computational power, DNNs struggle with distributional shifts and limited labeled data, leading to overfitting and …

abstract applications artificial artificial intelligence arxiv cs.ai cs.cv cs.lg cs.ne data distribution domain intelligence meta meta-learning networks neural networks performance survey testing through training type world