Year: 2022
Type: preprint
Abstract: Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender (conditioning on sensitive information), which in turn reduces undesirable information from the learned representations; weakly supervised contrastive learning constructs positive pairs with similar annotative attributes (conditioning on auxiliary information), which in turn are incorporated into the representations. Although conditional contrastive learning enables many applications, the conditional sampling procedure can be challenging if we cannot obtain sufficient data pairs for some values of the conditioning variable. This paper presents Conditional Contrastive Learning with Kernel (CCL-K) that converts existing conditional contrastive objectives into alternative forms that mitigate the insufficient data problem. Instead of sampling data according to the value of the conditioning variable, CCL-K uses the Kernel Conditional Embedding Operator that samples data from all available data and assigns weights to each sampled data given the kernel similarity between the values of the conditioning variable. We conduct experiments using weakly supervised, fair, and hard negatives contrastive learning, showing CCL-K outperforms state-of-the-art baselines.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Track-D: Data-Driven Disease Prevention and Control in Animal Health, G1732624909, Interpretable graphical models for large multi-modal COPD data (R01HL159805), G3550935145, AF: RI: Medium: Collaborative Research: Understanding and Improving Optimization in Deep and Recurrent Networks, CAREER: Learning Nonverbal Signatures, SCH: INT: Collaborative Research: Dyadic Behavior Informatics for Psychotherapy Process and Outcome, Robust Predictors of Mania and Psychosis, G9000035652, G9000151595
OpenAlex ID: W4226046397