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
3.4 End-to-end Composite Training
Overall, the training objective can be formalized as a weighted sum of CCL and PCG objectives:
where λ1 and λ2 are tunable hyperparameters controlling the strength of contrastive learning and prototype grounding regularization. The end-to-end training objective can be represented as:
The tunable hyperparameter β controls the effect of generalization and robustness on the RCE framework. Note that a higher value of β makes the concept learning procedure brittle and unable to adapt to target domains. However, a very low value of β makes the concept learning procedure overfit on the source domain, implying a tradeoff between concept generalization and performance.
This paper is available on arxiv under CC BY 4.0 DEED license.
