I put this paper into 4o so i can check if it is relevant, so that you do not have to do this too here are the bullet points:
- Vision Transformers can be parallelized to reduce latency and improve optimization without sacrificing accuracy.
- Fine-tuning only the attention layers is often sufficient for adapting ViTs to new tasks or resolutions, saving compute and memory.
- Using MLP-based patch preprocessing improves performance in masked self-supervised learning by preserving patch independence.
There's something that tickles me about this paper's title. The thought that everyone should know these three things. The idea of going to my neighbor who's a retired K-12 teacher and telling her about how adding MLP-based patch pre-processing layers improves Bert-like self-supervised training based on patch masking.