Authors:
(1) Sanchit Sinha, University of Virginia ([email protected]);
(2) Guangzhi Xiong, University of Virginia ([email protected]);
(3) Aidong Zhang, University of Virginia ([email protected]).
Table of Links
3 Methodology and 3.1 Representative Concept Extraction
3.2 Self-supervised Contrastive Concept Learning
3.3 Prototype-based Concept Grounding
3.4 End-to-end Composite Training
4 Experiments and 4.1 Datasets and Networks
4.3 Evaluation Metrics and 4.4 Generalization Results
4.5 Concept Fidelity and 4.6 Qualitative Visualization
3.2 Self-supervised Contrastive Concept Learning
Even though the RCE framework generates representative concepts, the concepts extracted are adulterated with domain noise thus limiting their generalization. In addition, with limited training data, the concept extraction process is not robust. Self-supervised learning contrastive training objectives are the most commonly used paradigm [Thota and Leontidis, 2021] for learning robust visual features in images. We incorporate self-supervised contrastive learning to learn domain invariant concepts, termed CCL.
This paper is available on arxiv under CC BY 4.0 DEED license.
