213 citations · 240 across the 7 of their papers we have counts for
11 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)…
Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning
Yannick Le Cacheux, Adrian Popescu, Hervé Le Borgne
Zero-shot learning (ZSL) makes object recognition in images possible in absence of visual training data for a part of the classes from a dataset. When the number of classes is larg…
ScaIL: Classifier Weights Scaling for Class Incremental Learning
Eden Belouadah, Adrian Popescu
Incremental learning is useful if an AI agent needs to integrate data from a stream. The problem is non trivial if the agent runs on a limited computational budget and has a bounde…
DeeSIL: Deep-Shallow Incremental Learning
Eden Belouadah, Adrian Popescu
Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach,…
Scalable domain adaptation of convolutional neural networks
Adrian Popescu, Etienne Gadeski, Hervé Le Borgne
Convolutional neural networks (CNNs) tend to become a standard approach to solve a wide array of computer vision problems. Besides important theoretical and practical advances in t…
On Deep Representation Learning from Noisy Web Images
Phong D. Vo, Alexandru Ginsca, Hervé Le Borgne +1
The keep-growing content of Web images may be the next important data source to scale up deep neural networks, which recently obtained a great success in the ImageNet classificatio…