  How do humans learn to acquire a powerful, flexible and robust representation
of objects? While much of this process remains unknown, it is clear that humans
do not require millions of object labels. Excitingly, recent algorithmic
advancements in self-supervised learning now enable convolutional neural
networks (CNNs) to learn useful visual object representations without
supervised labels, too. In the light of this recent breakthrough, we here
compare self-supervised networks to supervised models and human behaviour. We
tested models on 15 generalisation datasets for which large-scale human
behavioural data is available (130K highly controlled psychophysical trials).
Surprisingly, current self-supervised CNNs share four key characteristics of
their supervised counterparts: (1.) relatively poor noise robustness (with the
notable exception of SimCLR), (2.) non-human category-level error patterns,
(3.) non-human image-level error patterns (yet high similarity to supervised
model errors) and (4.) a bias towards texture. Taken together, these results
suggest that the strategies learned through today's supervised and
self-supervised training objectives end up being surprisingly similar, but
distant from human-like behaviour. That being said, we are clearly just at the
beginning of what could be called a self-supervised revolution of machine
vision, and we are hopeful that future self-supervised models behave
differently from supervised ones, and---perhaps---more similar to robust human
object recognition.
