  In this work, we propose a new unsupervised image segmentation approach based
on mutual information maximization between different constructed views of the
inputs. Taking inspiration from autoregressive generative models that predict
the current pixel from past pixels in a raster-scan ordering created with
masked convolutions, we propose to use different orderings over the inputs
using various forms of masked convolutions to construct different views of the
data. For a given input, the model produces a pair of predictions with two
valid orderings, and is then trained to maximize the mutual information between
the two outputs. These outputs can either be low-dimensional features for
representation learning or output clusters corresponding to semantic labels for
clustering. While masked convolutions are used during training, in inference,
no masking is applied and we fall back to the standard convolution where the
model has access to the full input. The proposed method outperforms current
state-of-the-art on unsupervised image segmentation. It is simple and easy to
implement, and can be extended to other visual tasks and integrated seamlessly
into existing unsupervised learning methods requiring different views of the
data.
