  We introduce a novel principle for self-supervised feature learning based on
the discrimination of specific transformations of an image. We argue that the
generalization capability of learned features depends on what image
neighborhood size is sufficient to discriminate different image
transformations: The larger the required neighborhood size and the more global
the image statistics that the feature can describe. An accurate description of
global image statistics allows to better represent the shape and configuration
of objects and their context, which ultimately generalizes better to new tasks
such as object classification and detection. This suggests a criterion to
choose and design image transformations. Based on this criterion, we introduce
a novel image transformation that we call limited context inpainting (LCI).
This transformation inpaints an image patch conditioned only on a small
rectangular pixel boundary (the limited context). Because of the limited
boundary information, the inpainter can learn to match local pixel statistics,
but is unlikely to match the global statistics of the image. We claim that the
same principle can be used to justify the performance of transformations such
as image rotations and warping. Indeed, we demonstrate experimentally that
learning to discriminate transformations such as LCI, image warping and
rotations, yields features with state of the art generalization capabilities on
several datasets such as Pascal VOC, STL-10, CelebA, and ImageNet. Remarkably,
our trained features achieve a performance on Places on par with features
trained through supervised learning with ImageNet labels.
