SVD-Based Typicality Maps for Out-of-Distribution Detection in Vision Transformers
Read the original on arXiv Computer Vision →The paper introduces a technique for examining Vision Transformers by decomposing each affine layer’s weight matrix with Singular Value Decomposition and projecting activations onto the leading right singular vectors, yielding compact, layer‑intrinsic representations. By fitting class‑conditional density models at each layer, the authors generate per‑class typicality scores that are stacked into two‑dimensional typicality maps, summarizing how class‑specific evidence evolves through the network. From these maps, two post‑hoc out‑of‑distribution detection scores are derived: the Prototype Alignment Score (PAS), which measures agreement with class reference prototypes, and the Multi‑Layer Soft Voting (MLSV) score, which captures cross‑layer consensus without stored prototypes, achieving competitive performance on ViT‑B/16 fine‑tuned on CIFAR‑100 without retraining or OOD exposure.
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