In this work, we propose a novel contrastive learning method to effectively find hard negative samples from the data bias perspective. We employ triplet loss [ ...
We introduce a novel debiased contrastive learning method to explore hard negatives by relative difficulty referencing the bias amplifying counterpart.
This is repository for NeurIPS 2022 Submission of Difficulty based contrastive learning. The code is based on SIMCLR (fundamental contrastive learning paper).
We introduce a novel debiased contrastive learning method to explore hard negatives by relative difficulty referencing the bias amplifying counterpart.
Contributions. • Propose a novel debiased contrastive learning method that addresses the problem from a new perspective by incorporating relative difficulty ...
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Posted: Sep 3, 2023
Posted: Sep 3, 2023
Difficulty-based sampling for debiased contrastive representation learning. T Jang, X Wang. Proceedings of the IEEE/CVF Conference on Computer Vision and ...
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A new class of unsupervised methods for selecting hard negative samples where the user can control the amount of hardness are developed.
We develop a new, debiased contrastive objective that corrects for the sampling bias of negative examples, while only assuming access to positive examples and ...
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