self-supervision

We show empirically that increasing the density of negative samples improves the basic model, and using a global negative queue further improves and stabilizes the model while training with hard negative samples.

We propose UXLA, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios.