Enhancing Contrastive Learning with False Negatives via Semi-Supervised Consistency Loss

In the field of machine learning, researchers have been exploring a technique called contrastive learning to improve the performance of various tasks. However, this approach relies on negative samples, which can be challenging to obtain without proper supervision. In the context of instance discrimination, where the goal is to learn an encoder that can distinguish positive pairs from negative ones, false negatives can severely hinder the learning process.
To address this issue, the authors propose a novel method that leverages the concept of semantic similarity to ensure the genuineness of negative samples. They argue that simply using augmentations or other views/modalities as positives is not sufficient, as these samples may not capture the true semantic content of the data. Instead, they suggest incorporating domain-level information into the training process to improve the quality of negative samples.
The authors demonstrate the effectiveness of their approach through experiments on several benchmark datasets. They show that their method outperforms existing contrastive learning techniques and provides more robust representations of instances. Furthermore, they provide a detailed analysis of the impact of false negatives on the learning process, highlighting the importance of addressing this issue in contrastive learning.
In summary, the authors of this article aim to improve the performance of contrastive learning by addressing the challenge of false negatives under the instance discrimination task. They propose a novel method that leverages domain-level information to ensure the genuineness of negative samples and demonstrate its effectiveness through experimental results. This work has significant implications for improving the efficiency and accuracy of machine learning algorithms in various applications.