Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation

Abstract

Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks when massive labeled data are available in the source domain but very few labeled samples are provided in the target domain. Existing solutions usually focus on feature alignment between the two domains while paying little attention to the discrimination capability of learned representations in the target domain. In this paper, we present a novel and effective method, namely Effective Label Propagation (ELP), to tackle this problem by using effective inter-domain and intra-domain semantic information propagation. For inter-domain propagation, we propose a new cycle discrepancy loss to encourage consistency of semantic information between the two domains. For intra-domain propagation, we propose an effective self-training strategy to mitigate the noises in pseudo-labeled target domain data and improve the feature discriminability in the target domain. As a general method, our ELP can be easily applied to various domain adaptation approaches and can facilitate their feature discrimination in the target domain. Experiments on Office-Home and DomainNet benchmarks show ELP consistently improves the classification accuracy of mainstream SSDA methods by 2%~3%. Additionally, ELP also improves the performance of UDA methods as well (81.5% vs 86.1%), based on UDA experiments on the VisDA-2017 benchmark. Our source code and pre-trained models will be released soon.

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Learning Transferable Features with Deep Adaptation Networks
• Conditional Adversarial Domain Adaptation
• VisDA: The Visual Domain Adaptation Challenge
• Asymmetric Tri-training for Unsupervised Domain Adaptation
• The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale

Cited by in corpus (4)

• Inter-Domain Mixup for Semi-Supervised Domain Adaptation
• Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
• Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation
• Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer