  Many recent methods for unsupervised representation learning train models to
be invariant to different "views," or distorted versions of an input. However,
designing these views requires considerable trial and error by human experts,
hindering widespread adoption of unsupervised representation learning methods
across domains and modalities. 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, our learned views enable comparable transfer accuracy
to the well-tuned SimCLR augmentations -- despite not including transformations
like cropping or color jitter. Furthermore, our learned views significantly
outperform baseline augmentations on speech recordings (+9% points, on average)
and wearable sensor data (+17% points). Viewmakers can also be combined with
handcrafted views: they improve robustness to common image corruptions and can
increase transfer performance in cases where handcrafted views are less
explored. These results suggest that viewmakers may provide a path towards more
general representation learning algorithms -- reducing the domain expertise and
effort needed to pretrain on a much wider set of domains. Code is available at
https://github.com/alextamkin/viewmaker.
