Three things everyone should know about Vision Transformers

There&#x27;s something that tickles me about this paper&#x27;s title. The thought that <i>everyone</i> should know these three things. The idea of going to my neighbor who&#x27;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.

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:<p>- Vision Transformers can be parallelized to reduce latency and improve optimization without sacrificing accuracy.<p>- Fine-tuning only the attention layers is often sufficient for adapting ViTs to new tasks or resolutions, saving compute and memory.<p>- Using MLP-based patch preprocessing improves performance in masked self-supervised learning by preserving patch independence.