  Humans can identify objects following various spatial transformations such as
scale and viewpoint. This extends to novel objects, after a single presentation
at a single pose, sometimes referred to as online invariance. CNNs have been
proposed as a compelling model of human vision, but their ability to identify
objects across transformations is typically tested on held-out samples of
trained categories after extensive data augmentation. This paper assesses
whether standard CNNs can support human-like online invariance by training
models to recognize images of synthetic 3D objects that undergo several
transformations: rotation, scaling, translation, brightness, contrast, and
viewpoint. Through the analysis of models' internal representations, we show
that standard supervised CNNs trained on transformed objects can acquire strong
invariances on novel classes even when trained with as few as 50 objects taken
from 10 classes. This extended to a different dataset of photographs of real
objects. We also show that these invariances can be acquired in a
self-supervised way, through solving the same/different task. We suggest that
this latter approach may be similar to how humans acquire invariances.
