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Class-Attention in Image Transformers

CaiT

5 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

CaiT, or Class-Attention in Image Transformers, is a type of vision transformer with several design alterations upon the original ViT. First a new layer scaling approach called LayerScale is used, adding a learnable diagonal matrix on output of each residual block, initialized close to (but not at) 0, which improves the training dynamics. Secondly, class-attention layers are introduced to the architecture. This creates an architecture where the transformer layers involving self-attention between patches are explicitly separated from class-attention layers -- that are devoted to extract the content of the processed patches into a single vector so that it can be fed to a linear classifier.

Source: Going deeper with Image Transformers

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Transfer Learning2
Image Classification1
Knowledge Distillation1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with CaiT: 2021 to 2024, peak 2 2 0 2021: 2 papers 2021 2022: 1 paper 2022 2023: 1 paper 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Vision Transformers

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