36 citations · 81 across the 17 of their papers we have counts for
4 papers · 1 filter
Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting
Marcin Pietroń, Dominik Żurek, Kamil Faber +1
Continual Learning (CL) is a process in which there is still huge gap between human and deep learning model efficiency. Recently, many CL algorithms were designed. Most of them hav…
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection
Marcin Pietron, Dominik Zurek, Kamil Faber +1
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectur…
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning
Kamil Faber, Dominik Zurek, Marcin Pietron +3
Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in machine learning and develop mode…
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights
Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski +1
Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior. Lifelong learning represents an emerging trend, answering the need for…