36 citations · 81 across the 17 of their papers we have counts for
6 papers · 1 filter
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection
Łukasz Marcjan, Dorota Wilk-Kołodziejczyk, Roberto Corizzo
Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which inspection systems must be e…
PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection
Mateusz Smendowski, Kamil Faber, Piotr Nawrocki +2
Time series anomaly detection (TSAD) underpins applications in predictive maintenance, finance, and cloud computing, however performance remains sensitive to representation choices…
Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios
Kamil Faber, Mateusz Smendowski, Roberto Corizzo
Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes. CAD benchmarks, however, d…
Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts
Taylor Maxson, Roberto Corizzo, Yaning Wu +2
Class-level evaluation can conceal substantial performance disparities across subconcepts within the same class, causing models that perform well on average to fail on specific sub…
TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning
Dominik Żurek, Kamil Faber, Marcin Pietron +2
Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on previously learned tasks. This s…
DNS-GT: A Graph-based Transformer Approach to Learn Embeddings of Domain Names from DNS Queries
Massimiliano Altieri, Ronan Hamon, Roberto Corizzo +2
Network intrusion detection systems play a crucial role in the security strategy employed by organisations to detect and prevent cyberattacks. Such systems usually combine pattern…