  The most recent year has witnessed the success of applying the Vision
Transformer (ViT) for image classification. However, there are still evidences
indicating that ViT often suffers following two aspects, i) the high
computation and the memory burden from applying the multiple Transformer layers
for pre-training on a large-scale dataset, ii) the over-fitting when training
on small datasets from scratch. To address these problems, a novel method,
namely, Vision Reservoir computing (ViR), is proposed here for image
classification, as a parallel to ViT. By splitting each image into a sequence
of tokens with fixed length, the ViR constructs a pure reservoir with a nearly
fully connected topology to replace the Transformer module in ViT. Two kinds of
deep ViR models are subsequently proposed to enhance the network performance.
Comparative experiments between the ViR and the ViT are carried out on several
image classification benchmarks. Without any pre-training process, the ViR
outperforms the ViT in terms of both model and computational complexity.
Specifically, the number of parameters of the ViR is about 15% even 5% of the
ViT, and the memory footprint is about 20% to 40% of the ViT. The superiority
of the ViR performance is explained by Small-World characteristics, Lyapunov
exponents, and memory capacity.
