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Surface anomaly detection aims to localize abnormal regions in images. A representative approach is the reconstruction-based method, which detect defects via reconstruction errors using generative models trained on normal images. However, these methods cannot directly estimate local distortion levels, which are commonly used for products made of metal or resin. To address this issue, we propose DiffuDewarp, a novel method that directly estimates local distortions. Our approach defines a pseudo-deformation defect generation process as a new diffusion process based on localized warping.

The problem of load estimation from sensor signals holds significance in the field of intelligent manufacturing. The goal of this work is to estimate the axial and spindle load values in a Computer Numerical Control machine from input sensor readings like spindle speed, feed rate, tool positions, etc. This can be viewed as a standard regression problem. Here, we propose a novel deep learning based regression technique that incorporates regression within the stacked autoencoder framework.