arXiv:2608. 04702v1 Announce Type: cross Abstract: Benchmarking deep learning (DL) models for multi-label classification (MLC) of remote sensing images (RSI) typically yields rankings that do not generalize beyond the evaluated datasets.
By Maryam Gholami Shiri, Eva Tuba, Sa\v{s}o D\v{z}eroski, Tome Eftimov, Ana Nikolikj
arXiv:2608. 00135v1 Announce Type: new Abstract: Design and architectural archives encode expert human knowledge in graphical formats, providing a critical testbed for design-inspired Machine Learning (ML) challenges absent with typical computer vision benchmarks.
By Alexandros Haridis, Charles Zhou
arXiv:2608.23182v1 Announce Type: cross
Abstract: We present a comparative study of label-free metrics for assessing the quality of representations in deep neural networks to understand their reliabi...
By Daniel Richards Arputharaj, Daniel J\"onsson, Gabriel Eilertsen
arXiv:2602.01718v2 Announce Type: replace
Abstract: Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benc...
By Sora Nakai, Youssef Fadhloun, Kacem Mathlouthi, Kotaro Yoshida, Ganesh Talluri, Ioannis Mitliagkas, Hiroki Naganuma
Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly understood at scale. We present the first large-scale empirical study of 194 publicly available VLMs, including 16 model families, covering a wide range of model sizes, 24 training datasets, and three evaluation benchmarks, namely ImageNet (overall performance), CelebA (typical single-attribute bias), and UrbanCars (complex multi-attribute biases).
arXiv:2512. 22702v2 Announce Type: replace Abstract: Deep learning models have grown popular in time series applications.
By Valentina Moretti, Ivan Marisca, Cesare Alippi, Andrea Cini