arXiv AI July 28, 2026 By Mohamed Abdallah Salem, Nourhan Zein Diab

Structural Preservation Governs Data Augmentation in Deep Learning-Based Laser Speckle Material Classification

arXiv:2607. 22725v1 Announce Type: cross Abstract: Data augmentation is routinely used to improve generalization in image classification, but the assumptions underlying standard policies are poorly matched to coherent imaging.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

computer-vision

arXiv AI

3d ago

D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces

arXiv:2511.11286v4 Announce Type: replace-cross Abstract: Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenario...

By Ruoqi Wang, Haitao Wang, Shaojie Guo, Qiong Luo

computer-vision fine-tuning benchmarks safety

arXiv Machine Learning

Jul 9

ML-based approach to classification and generation of structured light propagation in turbulent media

arXiv:2604. 14208v2 Announce Type: replace-cross Abstract: We study the classification task of structured-light beams after propagation through a random turbulent medium.

By Aokun Wang, Anjali Nair, Zhongjian Wang, Guillaume Bal

diffusion benchmarks

arXiv AI

Aug 13

COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification

arXiv:2505. 18315v3 Announce Type: replace-cross Abstract: We introduce \textbf{CoLoRA} (Convolutional Low-Rank Adaptation), a parameter-efficient fine-tuning method for convolutional neural networks (CNNs).

By Mariano Rivera, Angello Hoyos

computer-vision fine-tuning

arXiv AI

Jun 3

Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting

arXiv:2606. 02661v1 Announce Type: cross Abstract: Accurate precipitation nowcasting is vital for disaster mitigation, but deep learning methods face a key trade-off: regression models produce over-smoothed, spectrally decaying predictions that blur convective details and violate turbulence power laws; diffusion models generate realistic yet unanchored hallucinations lacking physical grounding.

By Yunlong Zhou, Chen Zhao, Danyang Peng, Fanfan Ji, Xiao-Tong Yuan

llms diffusion benchmarks safety

arXiv Machine Learning

Jul 17

Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance

arXiv:2607. 14466v1 Announce Type: new Abstract: Noise injection is a well-known technique in stochastic optimization.

By Matt L. Wiemann, Peter Melchior, Andrew K. Saydjari

safety

arXiv Machine Learning

Aug 5

Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts

arXiv:2608. 03557v1 Announce Type: cross Abstract: Tabular-to-image methods that convert tabular data into visual representations have emerged as a novel paradigm for leveraging the high performance of deep learning models.

By Malena Loza, Felipe Grijalva, Eva Milara, Luis Bote-Curiel, Francisco J. Lara-Abelenda, David Chushig-Muzo

computer-vision benchmarks

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Related stories

D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces

ML-based approach to classification and generation of structured light propagation in turbulent media

COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification

Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting

Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance

Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts