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Found 16 papers, 9 papers with code
Continual Learning in the Presence of Repetition
Continual learning (CL) provides a framework for training models in ever-evolving environments.
Continual Learning by Three-Phase Consolidation
TPC (Three-Phase Consolidation) is here introduced as a simple but effective approach to continually learn new classes (and/or instances of known classes) while controlling forgetting of previous knowledge.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
Detecting Morphing Attacks via Continual Incremental Training
Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset -- also exploiting different data sources -- to perform a batch-based training procedure, make the development of robust models particularly challenging.
Avalanche: A PyTorch Library for Deep Continual Learning
Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time.
Class-Incremental Learning with Repetition
We propose two stochastic stream generators that produce a wide range of CIR streams starting from a single dataset and a few interpretable control parameters.
Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning
In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i. i. d.
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community.
Generative Negative Replay for Continual Learning
Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems.
Is Class-Incremental Enough for Continual Learning?
The ability of a model to learn continually can be empirically assessed in different continual learning scenarios.
Continual Learning at the Edge: Real-Time Training on Smartphone Devices
On-device training for personalized learning is a challenging research problem.
Avalanche: an End-to-End Library for Continual Learning
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
Latent Replay for Real-Time Continual Learning
Continual learning techniques, where complex models are incrementally trained on small batches of new data, can make the learning problem tractable even for CPU-only embedded devices enabling remarkable levels of adaptiveness and autonomy.
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates.
