Before revision:  To address this, we propose viewmaker networks: generative models that learn to produce input-dependent views for contrastive learning. We train this network jointly with an encoder network to produce adversarial \ell_pperturbations for an input, which yields challenging yet useful views without extensive human tuning. Our learned views, when applied to CIFAR-10,  enable comparable transfer accuracy to the the well-studied augmentations used for the SimCLR model.
After revision:  To address this, we propose viewmaker networks: generative models that learn to produce useful views from a given input. Viewmakers are stochastic bounded adversaries: they produce views by generating and then adding an \ell_p-bounded perturbation to the input, and are trained adversarially with respect to the main encoder network. Remarkably, when pretraining on CIFAR-10, enable comparable transfer accuracy to the the well-studied augmentations used for the SimCLR model.