Hugging Face Trending Papers July 23, 2026

Latent Variable-Mediated Cross-Learning for Few-Shot Acoustic Impedance Imaging

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Acoustic impedance imaging is a fundamental yet severely ill-posed problem in subsurface analysis: the seismic wavelet is unknown, observations are band-limited, and labeled well-log samples are extremely scarce (typically <1% of all traces). Existing semi-supervised deep learning methods mitigate few-shot problem by incorporating forward modeling, yet they either rely on inaccurate prior wavelet assumptions or introduce auxiliary networks, leading to unstable optimization and degraded performance.

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benchmarks

arXiv AI

Aug 25

Seismic Acoustic Impedance Inversion Framework Based on Conditional Latent Generative Diffusion Model

The paper presents a seismic acoustic impedance inversion framework that uses a conditional latent generative diffusion model. By performing inversion in latent space and incorporating a lightweight wavelet-based module, the method reduces training overhead and improves efficiency. Numerical and field experiments show high accuracy, strong generalization, and enhanced geological detail with fewer diffusion steps.

By Jie Chen, Hongling Chen, Jinghuai Gao, Chuangji Meng, Tao Yang, XinXin Liang

rag diffusion

arXiv Machine Learning

Sep 23

Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

The paper presents a framework that adapts general-purpose Vision Foundation Models (VFMs) to seismic data denoising using Parameter‑Efficient Fine‑Tuning with Low‑Rank Adaptation (LoRA). It introduces a kurtosis‑guided unsupervised test‑time adaptation module that updates only LoRA parameters to self‑calibrate for site‑specific noise without ground truth. Experiments on exploration seismic images and DAS data demonstrate that the approach matches or surpasses domain‑specific models and generalizes well to unseen cross‑site data.

By Jiahua Zhao, Umair bin Waheed, Jing Sun, Yang Cui, Nikos Savva, Eric Verschuur

fine-tuning

arXiv Machine Learning

Sep 10

Solving the Elastic Wave Equation with Physics-Informed Neural Networks: A Robust and Critical Assessment

arXiv:2609.07983v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) have recently emerged as a promising approach for solving Partial Differential Equations (PDEs), offering a me...

By Davide Staub, Ben Moseley

rag safety

arXiv AI

Jul 21

SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection

arXiv:2511. 21325v2 Announce Type: replace-cross Abstract: Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs.

By Ido Nitzan Hidekel, Gal lifshitz, Khen Cohen, Dan Raviv

rag diffusion benchmarks safety

Hugging Face Trending Papers

Jun 25

A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets

Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of labelled experimental data and the high computational cost of generating large-scale high-fidelity simulation datasets.

fine-tuning

Hugging Face Trending Papers

Jun 29

A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition

Dual-energy CT (DECT) exploits attenuation differences across different X-ray spectra to provide richer material information and has been widely used in medical imaging. While sparse-view acquisition can lower radiation exposure, it makes DECT material decomposition even more challenging, as the problem is nonlinear and ill-posed.

efficiency