arXiv Machine Learning By Chongbiao Wang, Daniel Gaa, Joachim Weickert, Karl Schrader

The Neural Echo: A Signal Processing Perspective for Understanding Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2608. 04864v1 Announce Type: cross Abstract: We introduce the neural echo as a tool for understanding the behavior of neural networks.

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arXiv Computer Vision
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How Do DiTs Denoise? An Aggregator-Predictor View of Attention and MLPs

The paper investigates how diffusion transformers (DiTs) denoise images by framing them as aggregator–predictor models that use context aggregation and patch prediction. It shows that for Gaussian Markov random field priors, a global Bayes denoiser can be approximated with a limited context, and that patchwise linear denoisers recover empirical local denoisers when aggregation is informative. Experiments confirm that DiTs trained on real images exhibit noise‑dependent denoising contexts and that replacing DiT MLPs with shared‑memory patch predictors yields competitive performance, while cross‑dataset component transplantation supports the proposed functional roles of attention and MLPs.

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arXiv Computer Vision
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SAR-FAH is a Frequency‑Adaptive Hybrid network that uses Neural Ordinary Differential Equations (NODEs) to despeckle Synthetic Aperture Radar (SAR) images. It separates homogeneous and heterogeneous regions in the frequency domain via wavelet transform, then applies a NODE‑based module to low‑frequency sub‑bands for smooth denoising and an enhanced U‑Net with deformable convolutions to high‑frequency sub‑bands for edge and texture preservation. Experiments on synthetic and real SAR data show that SAR‑FAH outperforms current state‑of‑the‑art despeckling methods both quantitatively and qualitatively.

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