Do Vision Transformers See Like Convolutional Neural Networks?
Convolutional neural networks (CNNs) have so far been the de-facto model for
visual data. Recent work has shown that (Vision) Transformer models (ViT) can
achieve comparable or even superior performance on image classification tasks.
This raises a central question: how are Vision Transformers solving these
tasks? Are they acting like convolutional networks, or learning entirely
different visual representations? Analyzing the internal representation
structure of ViTs and CNNs on image classification benchmarks, we find striking
differences between the two architectures, such as ViT having more uniform
representations across all layers. We explore how these differences arise,
finding crucial roles played by self-attention, which enables early aggregation
of global information, and ViT residual connections, which strongly propagate
features from lower to higher layers. We study the ramifications for spatial
localization, demonstrating ViTs successfully preserve input spatial
information, with noticeable effects from different classification methods.
Finally, we study the effect of (pretraining) dataset scale on intermediate
features and transfer learning, and conclude with a discussion on connections
to new architectures such as the MLP-Mixer.