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.
arXiv:2608. 15802v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries.
Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries. Recent vision-language detectors imp...
arXiv:2409. 10094v3 Announce Type: replace-cross Abstract: Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.
arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.
arXiv:2607. 12094v1 Announce Type: cross Abstract: Reliable detection of out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models.
arXiv:2610.27211v1 Announce Type: new Abstract: Continual vision-language models (VLMs) encounter a stream of environments and must also reject inputs that do not belong to the environment at hand....
arXiv:2606. 08156v1 Announce Type: cross Abstract: Vision Transformers (ViTs) achieve strong performance but suffer from high computational costs due to quadratic self-attention complexity.
arXiv:2608. 04190v1 Announce Type: new Abstract: Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as majority voting trade recall for precision and are brittle to coordinated failures.
arXiv:2610.33909v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection identifies inputs outside the training distribution. Rank-based analyses such as RankFeat and RankWeight suggest...
arXiv:2609.01027v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need ima...
arXiv:2509. 12760v5 Announce Type: replace Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax activation function, adding Similarity (i.
arXiv:2604. 08572v2 Announce Type: replace Abstract: State-of-the-art post-hoc out-of-distribution detection methods rely on intermediate layer activation editing.