We introduce the neural echo as a tool for understanding the behavior of neural networks. It generalizes the model-based concepts of impulse responses, diffusion echoes, and filter echoes to learning-based methods.
arXiv:2608. 04864v1 Announce Type: cross Abstract: We introduce the neural echo as a tool for understanding the behavior of neural networks.
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.
arXiv:2607. 15693v1 Announce Type: cross Abstract: We describe a model of perceptual inference in primary visual cortex (V1) equivalent to a minimal diffusion model whose function can be readily understood from its parameters.
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.
arXiv:2411.15921v3 Announce Type: replace Abstract: The removal of multiplicative Gamma noise is a critical research area in the application of synthetic aperture radar (SAR) imaging, where neural ne...
arXiv:2503. 22223v2 Announce Type: replace Abstract: The semi-airborne transient electromagnetic method (SATEM) is capable of conducting rapid surveys over large-scale and hard-to-reach areas.
arXiv:2504.10201v3 Announce Type: replace Abstract: In this paper, we introduce a synthetic image generator relying on a few simple principles, specifically focusing on geometric modeling, textures,...
arXiv:2506. 14202v4 Announce Type: replace-cross Abstract: End-to-end backpropagation requires storing activations throughout all layers, creating memory bottlenecks that limit model scalability.
arXiv:2609.22807v1 Announce Type: new Abstract: Implicit Neural Representation (INR) leverages neural networks to represent discrete signals such as images as continuous ones, where the network weigh...
arXiv:2606. 08309v1 Announce Type: new Abstract: Score-based generative models have had remarkable success over the last decade in generating a diverse set of visually plausible images.
The paper revisits feature inversion via inverse networks, proposing a modular variant that is computationally efficient and can produce more semantically coherent image reconstructions. It applies this method to large‑scale transformer‑based vision models—Detection Transformer, Vision Transformer, Swin Transformer, and Data‑Efficient Image Transformer—examining reconstructions across network depth. The analysis highlights gradual representational changes in Detection and Vision Transformers, systematic differences in preserving contextual shape and fine‑grained details, and varying robustness to color perturbations, while also demonstrating the framework’s broader applicability to other transformer architectures.
arXiv:2606. 26204v1 Announce Type: new Abstract: Floods frequently impact regions around the world.