  We propose ADIOS, a masked image model (MIM) framework for self-supervised
learning, which simultaneously learns a masking function and an image encoder
using an adversarial objective. The image encoder is trained to minimise the
distance between representations of the original and that of a masked image.
The masking function, conversely, aims at maximising this distance. ADIOS
consistently improves on state-of-the-art self-supervised learning (SSL)
methods on a variety of tasks and datasets -- including classification on
ImageNet100 and STL10, transfer learning on CIFAR10/100, Flowers102 and
iNaturalist, as well as robustness evaluated on the backgrounds challenge (Xiao
et al., 2021) -- while generating semantically meaningful masks. Unlike modern
MIM models such as MAE, BEiT and iBOT, ADIOS does not rely on the image-patch
tokenisation construction of Vision Transformers, and can be implemented with
convolutional backbones. We further demonstrate that the masks learned by ADIOS
are more effective in improving representation learning of SSL methods than
masking schemes used in popular MIM models. Code is available at
https://github.com/YugeTen/adios.
