Domain generalization: A survey
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 …
challenging for machines to reproduce. This is because most learning algorithms strongly …
Edge-cloud polarization and collaboration: A comprehensive survey for ai
Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …
Fishr: Invariant gradient variances for out-of-distribution generalization
Learning robust models that generalize well under changes in the data distribution is critical
for real-world applications. To this end, there has been a growing surge of interest to learn …
for real-world applications. To this end, there has been a growing surge of interest to learn …
On learning contrastive representations for learning with noisy labels
Deep neural networks are able to memorize noisy labels easily with a softmax cross entropy
(CE) loss. Previous studies attempted to address this issue focus on incorporating a noise …
(CE) loss. Previous studies attempted to address this issue focus on incorporating a noise …
Invariant information bottleneck for domain generalization
Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain
generalization. Nevertheless, the loss function is difficult to optimize for nonlinear classifiers …
generalization. Nevertheless, the loss function is difficult to optimize for nonlinear classifiers …
Environment-aware dynamic graph learning for out-of-distribution generalization
Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-
temporal patterns on dynamic graphs. However, existing works fail to generalize under …
temporal patterns on dynamic graphs. However, existing works fail to generalize under …
Graph domain adaptation via theory-grounded spectral regularization
Transfer learning on graphs drawn from varied distributions (domains) is in great demand
across many applications. Emerging methods attempt to learn domain-invariant …
across many applications. Emerging methods attempt to learn domain-invariant …
Federated domain generalization: A survey
Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …
identical and that data is centrally stored for training and testing. However, in real-world …
Prior knowledge guided unsupervised domain adaptation
The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an
attractive technique in many real-world applications, though it also brings great challenges …
attractive technique in many real-world applications, though it also brings great challenges …
Learning distinctive margin toward active domain adaptation
Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under
unsupervised or few-shot semi-supervised settings, recently the solution of active learning …
unsupervised or few-shot semi-supervised settings, recently the solution of active learning …