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arXiv Machine Learning By Elias Krey, Nils Neukirch, Nils Strodthoff

How Far Does a Shared Linear Map Go? Probing Feature-Space Manipulability for Image Editing

Read the original on arXiv Machine Learning →

The paper investigates how well a simple spatially shared linear map can predict changes in a model’s internal feature space caused by various image manipulations, including geometric, photometric, occlusion, and diffusion-generated semantic edits. Experiments across ConvNeXt, SwinV2, and DINOv3 show that this linear operator often performs nearly as well as more complex, higher‑capacity probes, especially in deeper layers of supervised backbones. The study concludes that a shared linear map is frequently sufficient to capture diverse image edits, with its leading singular components encoding semantic content and higher‑rank components refining details.

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 Machine Learning.

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