Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation

by   Jichang Li, et al.
Association for Computing Machinery
The University of Hong Kong

In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source domain. This could lead to disconnection between the labeled and unlabeled target samples as well as misalignment between unlabeled target samples and the source domain. In this paper, we propose a novel approach called Cross-domain Adaptive Clustering to address this problem. To achieve both inter-domain and intra-domain adaptation, we first introduce an adversarial adaptive clustering loss to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains. We further apply pseudo labeling to unlabeled samples in the target domain and retain pseudo-labels with high confidence. Pseudo labeling expands the number of “labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning. Extensive experiments on benchmark datasets, including DomainNet, Office-Home and Office, demonstrate that our proposed approach achieves the state-of-the-art performance in semi-supervised domain adaptation.


Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning

In semi-supervised domain adaptation (SSDA), a few labeled target sample...

Semi-Supervised Domain Adaptation by Similarity based Pseudo-label Injection

One of the primary challenges in Semi-supervised Domain Adaptation (SSDA...

Adversarial Variational Domain Adaptation

In this work we address the problem of transferring knowledge obtained f...

Pred Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptation

Semi-supervised domain adaptation aims to classify data belonging to a t...

Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples

Domain adaptation aims to generalize a model from a source domain to tac...

Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification

Semi-supervised domain adaptation is a technique to build a classifier f...

Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation

Heterogeneous Domain Adaptation (HDA) aims to enable effective transfer ...

Please sign up or login with your details

Forgot password? Click here to reset