  In this work we revisit the most fundamental building block in deep learning,
the multi-layer perceptron (MLP), and study the limits of its performance on
vision tasks. Empirical insights into MLPs are important for multiple reasons.
(1) Given the recent narrative "less inductive bias is better", popularized due
to transformers eclipsing convolutional models, it is natural to explore the
limits of this hypothesis. To that end, MLPs offer an ideal test bed, as they
lack any vision-specific inductive bias. (2) MLPs have almost exclusively been
the main protagonist in the deep learning theory literature due to their
mathematical simplicity, serving as a proxy to explain empirical phenomena
observed for more complex architectures. Surprisingly, experimental datapoints
for MLPs are very difficult to find in the literature, especially when coupled
with large pre-training protocols. This discrepancy between practice and theory
is worrying: Do MLPs reflect the empirical advances exhibited by practical
models? Or do theorists need to rethink the role of MLPs as a proxy? We provide
insights into both these aspects. We show that the performance of MLPs
drastically improves with scale (95% on CIFAR10, 82% on CIFAR100, 58% on
ImageNet ReaL), highlighting that lack of inductive bias can indeed be
compensated. We observe that MLPs mimic the behaviour of their modern
counterparts faithfully, with some components in the learning setting however
exhibiting stronger or unexpected behaviours. Due to their inherent
computational efficiency, large pre-training experiments become more accessible
for academic researchers. All of our experiments were run on a single GPU.
