Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods
arxiv(2023)
摘要
Self-supervised learning algorithms (SSL) based on instance discrimination
have shown promising results, performing competitively or even outperforming
supervised learning counterparts in some downstream tasks. Such approaches
employ data augmentation to create two views of the same instance (i.e.,
positive pairs) and encourage the model to learn good representations by
attracting these views closer in the embedding space without collapsing to the
trivial solution. However, data augmentation is limited in representing
positive pairs, and the repulsion process between the instances during
contrastive learning may discard important features for instances that have
similar categories. To address this issue, we propose an approach to identify
those images with similar semantic content and treat them as positive
instances, thereby reducing the chance of discarding important features during
representation learning and increasing the richness of the latent
representation. Our approach is generic and could work with any self-supervised
instance discrimination frameworks such as MoCo and SimSiam. To evaluate our
method, we run experiments on three benchmark datasets: ImageNet, STL-10 and
CIFAR-10 with different instance discrimination SSL approaches. The
experimental results show that our approach consistently outperforms the
baseline methods across all three datasets; for instance, we improve upon the
vanilla MoCo-v2 by 4.1
epochs. We also report results on semi-supervised learning, transfer learning
on downstream tasks, and object detection.
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