Optimizing transformations for contrastive learning in a differentiable
framework
Optimizing transformations for contrastive learning in a differentiable
framework
Current contrastive learning methods use random transformations sampled from a large list of transformations, with fixed hyperparameters, to learn invariance from an unannotated database. Following previous works that introduce a small amount of supervision, we propose a framework to find optimal transformations for contrastive learning using a differentiable transformation network. …