Feature Space Analysis by Guided Diffusion Model

The paper introduces a decoder that generates images whose features closely match a user-specified feature, enabling detailed analysis of a vision-related deep neural network’s feature space. Implemented as a guided diffusion model, it steers a pre-trained diffusion model to minimize the Euclidean distance between the feature of a clean image and the target feature at each generation step. The method is training‑free, works on a single COTS GPU, and has been validated on CLIP’s image encoder and ResNet‑50, showing high feature‑matching accuracy and practical feasibility.

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• diffusion