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arXiv Computer Vision By Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari

Technical note on: Zero-Training Feature-Space Alignment via Information Geometry

Read the original on arXiv Computer Vision →

The paper introduces Zero-Training Fisher Geometry Alignment (ZFGA), a closed‑form test‑time adaptation technique that aligns the Fisher geometry of test‑time feature embeddings with a reference geometry from clean data, improving robustness to covariate shift without altering model parameters. ZFGA is evaluated on CIFAR‑10‑C and ImageNet‑C with ResNet‑50, DINO ViT‑S/16, and CLIP ViT‑B/32, consistently outperforming zero‑shot inference and remaining stable across all three model families. Compared to other training‑free and gradient‑based methods, ZFGA uniquely avoids substantial performance degradation and requires only forward passes and matrix operations at inference time, making it a lightweight, deterministic alternative to optimization‑based test‑time adaptation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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