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[Submitted on 14 Aug 2024] View a PDF of the paper titled Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection, by Jordan F. Masakuna and 4 other authors View PDF HTML (experimental) Abstract:Training data sets intended for unsupervised anomaly detection, typically presumed to be anomaly-free, often contain anomalies (or contamination), a challenge that significantly undermines model performance. Most robust unsupervised anomaly detection models rely on contamination ratio information to tackle contamination. However, in reality, contamination ratio may be inaccurate. We investigate on the impact of inaccurate contamination ratio information in robust unsupervised anomaly detection. We verify whether they…

[Submitted on 14 Aug 2024] View a PDF of the paper titled NeuroPapyri: A Deep Attention Embedding Network for Handwritten Papyri Retrieval, by Giuseppe De Gregorio and 4 other authors View PDF HTML (experimental) Abstract:The intersection of computer vision and machine learning has emerged as a promising avenue for advancing historical research, facilitating a more profound exploration of our past. However, the application of machine learning approaches in historical palaeography is often met with criticism due to their perceived ``black box'' nature. In response to this challenge, we introduce NeuroPapyri, an innovative deep learning-based model specifically designed for the analysis…