aiqbench analysis
This paper introduces EvoCL, a novel gradient-free continual learning method that leverages evolutionary algorithms to mitigate catastrophic forgetting without requiring access to historical data or gradients. By utilizing lightweight adapter modules to translate past task representations into the current model space, this approach effectively addresses memory and privacy constraints inherent in standard training pipelines. The results demonstrate competitive performance across benchmarks under strict resource limitations, offering a practical alternative for deploying AI systems where data retention is prohibited.
Gradient-Free Continual Learning
arXiv:2504.01219v3 Announce Type: replace Abstract: Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting. Standard continual learning techniques try to fix this by saving old data or relying on complex gradient updates, but these methods fail when past data cannot be stored due to memory or privacy constraints. To solve this, we propose EvoCL, a gradient-free approach that uses evolutionary algorithms to update the network without needing old data or gradients. EvoCL uses a lightweight adapter module to translate saved representations from past tasks into the model's current space, allowing it to learn new tasks while keeping past knowledge intact. Across multiple benchmarks, EvoCL matches or exceeds standard performance under strict memory constraints, offering a simple and flexible new direction for continual learning. The code to reproduce these results is available at https://github.com/grypesc/EvoCL.
Abstract:Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting. Standard continual learning techniques try to fix this by saving old data or relying on complex gradient updates, but these methods fail when past data cannot be stored due to memory or privacy constraints. To solve this, we propose EvoCL, a gradient-free approach that uses evolutionary algorithms to update the network without needing old data or gradients. EvoCL uses a lightweight adapter module to translate saved representations from past tasks into the model's current space, allowing it to learn new tasks while keeping past knowledge intact. Across multiple benchmarks, EvoCL matches or exceeds standard performance under strict memory constraints, offering a simple and flexible new direction for continual learning. The code to reproduce these results is available at this https URL.
Machine Learning (cs.LG)
arXiv:2504.01219 [cs.LG]
(or arXiv:2504.01219v3 [cs.LG] for this version)
Full text under the publisher's open licence · 151 words
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