Supervised Contrastive Learning with Hard Negative Samples

Code

• github.com/rjiang03/h-scl
  OfficialIn paperpytorch★ 10

Abstract

Through minimization of an appropriate loss function such as the InfoNCE loss, contrastive learning (CL) learns a useful representation function by pulling positive samples close to each other while pushing negative samples far apart in the embedding space. The positive samples are typically created using "label-preserving" augmentations, i.e., domain-specific transformations of a given datum or anchor. In absence of class information, in unsupervised CL (UCL), the negative samples are typically chosen randomly and independently of the anchor from a preset negative sampling distribution over the entire dataset. This leads to class-collisions in UCL. Supervised CL (SCL), avoids this class collision by conditioning the negative sampling distribution to samples having labels different from that of the anchor. In hard-UCL (H-UCL), which has been shown to be an effective method to further enhance UCL, the negative sampling distribution is conditionally tilted, by means of a hardening function, towards samples that are closer to the anchor. Motivated by this, in this paper we propose hard-SCL (H-SCL) wherein the class conditional negative sampling distribution is tilted via a hardening function. Our simulation results confirm the utility of H-SCL over SCL with significant performance gains in downstream classification tasks. Analytically, we show that in the limit of infinite negative samples per anchor and a suitable assumption, the H-SCL loss is upper bounded by the H-UCL loss, thereby justifying the utility of H-UCL for controlling the H-SCL loss in the absence of label information. Through experiments on several datasets, we verify the assumption as well as the claimed inequality between H-UCL and H-SCL losses. We also provide a plausible scenario where H-SCL loss is lower bounded by UCL loss, indicating the limited utility of UCL in controlling the H-SCL loss.

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Reproductions