Abstract
The use of episodic memories in continual learning has been shown to be effective in terms of alleviating catastrophic forgetting. In recent studies, several gradient-based approaches have been developed to make more efficient use of compact episodic memories, which constrain the gradients resulting from new samples with those from memorized samples, aiming to reduce the diversity of gradients from different tasks. In this paper, we reveal the relation between diversity of gradients and discriminativeness of representations, demonstrating connections between Deep Metric Learning and continual learning. Based on these findings,we propose a simple yet highly efficient method - Discriminative Representation Loss (DRL) - for continual learning. In comparison with several state-of-the-art methods, DRL shows effectiveness with low computational cost on multiple benchmark experiments in the setting of online continual learning.
Original language | English |
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Number of pages | 15 |
Journal | arXiv |
Publication status | Unpublished - 19 Jun 2020 |
Keywords
- stat.ML
- cs.LG
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Dive into the research topics of 'Discriminative Representation Loss (DRL): A More Efficient Approach Than Gradient Re-projection in continual learning'. Together they form a unique fingerprint.Student theses
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Efficient Continual Learning: Approaches and Measures
Chen, Y. (Author), Flach, P. (Supervisor) & Diethe, T. (Supervisor), 28 Sept 2021Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)
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HPC (High Performance Computing) and HTC (High Throughput Computing) Facilities
Alam, S. R. (Manager), Williams, D. A. G. (Manager), Eccleston, P. E. (Manager) & Greene, D. (Manager)
Facility/equipment: Facility