213 citations · 230 across the 4 of their papers we have counts for
9 papers
Minority Class Oriented Active Learning for Imbalanced Datasets
Umang Aggarwal, Adrian Popescu, Céline Hudelot
Active learning aims to optimize the dataset annotation process when resources are constrained. Most existing methods are designed for balanced datasets. Their practical applicabil…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
Eden Belouadah, Adrian Popescu, Ioannis Kanellos
The ability of artificial agents to increment their capabilities when confronted with new data is an open challenge in artificial intelligence. The main challenge faced in such cas…
Unveiling Real-Life Effects of Online Photo Sharing
Van-Khoa Nguyen, Adrian Popescu, Jerome Deshayes-Chossart
Social networks give free access to their services in exchange for the right to exploit their users' data. Data sharing is done in an initial context which is chosen by the users.…
Initial Classifier Weights Replay for Memoryless Class Incremental Learning
Eden Belouadah, Adrian Popescu, Ioannis Kanellos
Incremental Learning (IL) is useful when artificial systems need to deal with streams of data and do not have access to all data at all times. The most challenging setting requires…
Active Class Incremental Learning for Imbalanced Datasets
Eden Belouadah, Adrian Popescu, Umang Aggarwal +1
Incremental Learning (IL) allows AI systems to adapt to streamed data. Most existing algorithms make two strong hypotheses which reduce the realism of the incremental scenario: (1)…