  Learning image representations without human supervision is an important and
active research field. Several recent approaches have successfully leveraged
the idea of making such a representation invariant under different types of
perturbations, especially via contrastive-based instance discrimination
training. Although effective visual representations should indeed exhibit such
invariances, there are other important characteristics, such as encoding
contextual reasoning skills, for which alternative reconstruction-based
approaches might be better suited.
  With this in mind, we propose a teacher-student scheme to learn
representations by training a convnet to reconstruct a bag-of-visual-words
(BoW) representation of an image, given as input a perturbed version of that
same image. Our strategy performs an online training of both the teacher
network (whose role is to generate the BoW targets) and the student network
(whose role is to learn representations), along with an online update of the
visual-words vocabulary (used for the BoW targets). This idea effectively
enables fully online BoW-guided unsupervised learning. Extensive experiments
demonstrate the interest of our BoW-based strategy which surpasses previous
state-of-the-art methods (including contrastive-based ones) in several
applications. For instance, in downstream tasks such Pascal object detection,
Pascal classification and Places205 classification, our method improves over
all prior unsupervised approaches, thus establishing new state-of-the-art
results that are also significantly better even than those of supervised
pre-training. We provide the implementation code at
https://github.com/valeoai/obow.
