Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively …
Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as …
Y Zhang, M Li, R Li, K Jia… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. With the …
Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen …
Y Iwasawa, Y Matsuo - Advances in Neural Information …, 2021 - proceedings.neurips.cc
This paper presents a new algorithm for domain generalization (DG),\textit {test-time template adjuster (T3A)}, aiming to robustify a model to unknown distribution shift. Unlike …
Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data. Domain generalization aims at …
Q Liu, C Chen, J Qin, Q Dou… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in …
Abstract Domain generalization (DG) aims to generalize a model trained on multiple source (ie, training) domains to a distributionally different target (ie, test) domain. In contrast to the …
Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains. Domain …