Background: Catastrophic forgetting is the notorious vulnerability of neural networks to the changes in the data distribution during learning. This phenomenon has long been considered a major obstacle for using learning agents in realistic continual learning settings. A large body of continual learning research assumes that task boundaries are known during training. However, only a few works consider scenarios in which task boundaries are unknown or not well defined -- task agnostic scenarios. The optimal Bayesian solution for this requires an intractable online Bayes update to the weights posterior. Contributions: We aim to approximate the online Bayes update as accurately as possible. To do so, we derive novel fixed-point equations for the online variational Bayes optimization problem, for multivariate Gaussian parametric distributions. By iterating the posterior through these fixed-point equations, we obtain an algorithm (FOO-VB) for continual learning which can handle non-stationary data distribution using a fixed architecture and without using external memory (i.e. without access to previous data). We demonstrate that our method (FOO-VB) outperforms existing methods in task agnostic scenarios. FOO-VB Pytorch implementation will be available online.
翻译:灾难性的遗忘是神经网络在学习期间对数据分配变化的脆弱性。 这种现象长期以来被认为是在现实的连续学习环境中使用学习剂的主要障碍。 大量的持续学习研究认为在培训期间任务界限是已知的。 然而,只有少数工作考虑了任务界限未知或定义不明确的情景 -- -- 任务不可知情景。 这方面的最佳贝叶斯式解决方案要求对重力后背体进行棘手的在线贝斯更新。 贡献 : 我们的目标是尽可能准确地接近在线贝斯更新。 为此,我们为在线变异性贝斯优化问题(多变性高斯的参数分布)推出新的固定点方程式。 通过通过这些固定点方程式对后方程式进行循环,我们获得了一种算法(FOOO-VB),用于持续学习,这种算法可以使用固定结构处理非静止数据传播,而不用外部记忆(例如没有访问先前的数据) 。 我们证明,我们的方法(FOOO-VB) 将比任务变异性假设方案的现有方法更符合在线实施方式。