无梯度持续学习
Gradient-Free Continual Learning
AI总结：
研究针对神经网络灾难性遗忘问题，提出无梯度方法EvoCL，利用进化算法更新网络，通过轻量级适配器模块转换过去任务表示，在严格内存限制下，该方法在多基准测试中表现良好，为持续学习提供新方向。
摘要：
神经网络在学习新技能时容易遗忘旧技能，即灾难性遗忘问题。标准持续学习技术通过保存旧数据或依赖复杂梯度更新来解决，但因内存或隐私限制无法存储旧数据时这些方法失效。为此提出EvoCL，一种无梯度方法，利用进化算法更新网络，无需旧数据或梯度。它使用轻量级适配器模块将过去任务的保存表示转换到模型当前空间，在严格内存限制下，EvoCL在多个基准测试中达到或超过标准性能，为持续学习提供了简单灵活的新方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
