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Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data
Jessica Plassmann, Nicolas Schuler, Georg von Freymann, Michael Schuth

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali
https://arxiv.org/abs/2510.10263

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association…

Deep Learning of the Biswas-Chatterjee-Sen Model
J. F. Silva Neto, D. S. M. Alencar, L. T. Brito, G. A. Alves, F. W. S. Lima, A. Macedo-Filho, R. S. Ferreira, T. F. A. Alves
https://arxiv.org/abs/2510.09446

Deep Learning of the Biswas-Chatterjee-Sen Model
We investigate the critical properties of kinetic continuous opinion dynamics using deep learning techniques. The system consists of $N$ continuous spin variables in the interval $[-1,1]$. Dense neural networks are trained on spin configuration data generated via kinetic Monte Carlo simulations, accurately identifying the critical point on both square and triangular lattices. Classical unsupervised learning with principal component analysis reproduces the magnetization and allows estimation of …

STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Disharee Bhowmick, Ranjith Ramanathan, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2510.09593

STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Time series data often contain latent temporal structure, transitions between locally stationary regimes, repeated motifs, and bursts of variability, that are rarely leveraged in standard representation learning pipelines. Existing models typically operate on raw or fixed-window sequences, treating all time steps as equally informative, which leads to inefficiencies, poor robustness, and limited scalability in long or noisy sequences. We propose STaTS, a lightweight, unsupervised framework for …

Unsupervised lexicon learning from speech is limited by representations rather than clustering
Danel Adendorff, Simon Malan, Herman Kamper
https://arxiv.org/abs/2510.09225 https…

Unsupervised lexicon learning from speech is limited by representations rather than clustering
Zero-resource word segmentation and clustering systems aim to tokenise speech into word-like units without access to text labels. Despite progress, the induced lexicons are still far from perfect. In an idealised setting with gold word boundaries, we ask whether performance is limited by the representation of word segments, or by the clustering methods that group them into word-like types. We combine a range of self-supervised speech features (continuous/discrete, frame/word-level) with differe…