  Borrowing from the transformer models that revolutionized the field of
natural language processing, self-supervised feature learning for visual tasks
has also seen state-of-the-art success using these extremely deep, isotropic
networks. However, the typical AI researcher does not have the resources to
evaluate, let alone train, a model with several billion parameters and
quadratic self-attention activations. To facilitate further research, it is
necessary to understand the features of these huge transformer models that can
be adequately studied by the typical researcher. One interesting characteristic
of these transformer models is that they remove most of the inductive biases
present in classical convolutional networks. In this work, we analyze the
effect of these and more inductive biases on small to moderately-sized
isotropic networks used for unsupervised visual feature learning and show that
their removal is not always ideal.
