Semantic Positive Pairs for Enhancing Contrastive Instance
  Discrimination
Mohammad Alkhalefi Georgios Leontidis Mingjun Zhong
Self-supervised learning algorithms based on instance discrimination
effectively prevent representation collapse and produce promising results in
representation learning. However, the process of attracting positive pairs
(i.e., two views of the same instance) in the embedding space and repelling all
other instances (i.e., negative pairs) irrespective of their categories could
result in discarding important features. To address this issue, we propose an
approach to identifying those images with similar semantic content and treating
them as positive instances, named semantic positive pairs set (SPPS), thereby
reducing the risk of discarding important features during representation
learning. Our approach could work with any contrastive instance discrimination
framework such as SimCLR or MOCO. We conduct experiments on three datasets:
ImageNet, STL-10 and CIFAR-10 to evaluate our approach. The experimental
results show that our approach consistently outperforms the baseline method
vanilla SimCLR across all three datasets; for example, our approach improves
upon vanilla SimCLR under linear evaluation protocol by 4.18% on ImageNet with
a batch size 1024 and 800 epochs.