Before revision:  Our learned views, when applied to CIFAR-10,  enable comparable transfer accuracy to the the well-studied augmentations used for the SimCLR model. Our views significantly outperforming baseline augmentations in speech (+9\% absolute ) and wearable sensor  (+17\% absolute)domains.
After revision:  Our learned views, when applied to CIFAR-10, 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\% absolute ) and wearable sensor (+17\% absolute)domains.