SECL: Sampling Enhanced Contrastive Learning.

Instance-level contrastive learning such as SimCLR has been successful as a powerful method for representation learning. However, SimCLR suffers from problems of sampling bias, feature bias and model collapse. A set-level based S ampling E nhanced C ontrastive L earning (SECL) method based on SimCLR is proposed in this paper. We use the proposed super-sampling method to expand the augmented samples into a contrastive-positive set, which can learn class features of the target sample to reduce the bias. The contrastive-positive set includes Augmentations (the original augmented samples) and Neighbors (the super-sampled samples). We also introduce a samples-correlation strategy to prevent model collapse, where a positive correlation loss or a negative correlation loss is computed to adjust the balance of model’s Alignment and Uniformity. SECL reaches 94.14% classification precision on SST-2 dataset and 89.25% on ARSC dataset. For the multi-class classification task, SECL achieves 90.99% on AGNews dataset. They are all about 1% higher than the precision of SimCLR. Experiments show that the training convergence of SECL is faster, and SECL reduces the risk of bias and model collapse.