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Deep clustering: A comprehensive survey
Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
A brief review of domain adaptation
Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …
distributions. Therefore, a model learned from the labeled training data is expected to …
Efficient test-time model adaptation without forgetting
Test-time adaptation provides an effective means of tackling the potential distribution shift
between model training and inference, by dynamically updating the model at test time. This …
between model training and inference, by dynamically updating the model at test time. This …
Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation
Adversarial learning has achieved remarkable performances for unsupervised domain
adaptation (UDA). Existing adversarial UDA methods typically adopt an additional …
adaptation (UDA). Existing adversarial UDA methods typically adopt an additional …
Contrastive adaptation network for unsupervised domain adaptation
Abstract Unsupervised Domain Adaptation (UDA) makes predictions for the target domain
data while manual annotations are only available in the source domain. Previous methods …
data while manual annotations are only available in the source domain. Previous methods …