  Vision Transformers (ViTs) outperforms convolutional neural networks (CNNs)
in several vision tasks with its global modeling capabilities. However, ViT
lacks the inductive bias inherent to convolution making it require a large
amount of data for training. This results in ViT not performing as well as CNNs
on small datasets like medicine and science. We experimentally found that
masked autoencoders (MAE) can make the transformer focus more on the image
itself, thus alleviating the data-hungry issue of ViT to some extent. Yet the
current MAE model is too complex resulting in over-fitting problems on small
datasets. This leads to a gap between MAEs trained on small datasets and
advanced CNNs models still. Therefore, we investigated how to reduce the
decoder complexity in MAE and found a more suitable architectural configuration
for it with small datasets. Besides, we additionally designed a location
prediction task and a contrastive learning task to introduce localization and
invariance characteristics for MAE. Our contrastive learning task not only
enables the model to learn high-level visual information but also allows the
training of MAE's class token. This is something that most MAE improvement
efforts do not consider. Extensive experiments have shown that our method shows
state-of-the-art performance on standard small datasets as well as medical
datasets with few samples compared to the current popular masked image modeling
(MIM) and vision transformers for small datasets.The code and models are
available at https://github.com/Talented-Q/SDMAE.
