基于生成式分类器的类别增量学习
Class-Incremental Learning with Generative Classifiers
AI总结：
针对深度神经网络增量学习新类别时现有方法存在缺陷或性能不足的问题，提出基于生成式分类的策略，通过学习联合分布结合贝叶斯规则分类，在多个基准上优于无数据存储的基线方法。
摘要：
对深度神经网络进行增量训练以识别新类别是一项具有挑战性的问题。现有大多数类别增量学习方法要么存储数据，要么使用生成重放，二者均存在缺陷；而参数正则化、偏差校正等“无排练”替代方案也无法持续实现高性能。本文提出一种新的类别增量学习策略：生成式分类。我们不直接学习条件分布p(y|x)，而是学习联合分布p(x,y)（分解为p(x|y)p(y)），并利用贝叶斯规则执行分类。作为原理验证，本文通过为每个待学习类别训练一个变分自编码器，并使用重要性采样估计似然p(x|y)来实现该策略。这种简单方法在多种持续学习基准上表现优异，性能优于生成重放及其他不存储数据的现有基线方法。
英文摘要：
Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay, both of which have drawbacks, while 'rehearsal-free' alternatives such as parameter regularization or bias-correction methods do not consistently achieve high performance. Here, we put forward a new strategy for class-incremental learning: generative classification. Rather than directly learning the conditional distribution p(y|x), our proposal is to learn the joint distribution p(x,y), factorized as p(x|y)p(y), and to perform classification using Bayes' rule. As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned and by using importance sampling to estimate the likelihoods p(x|y). This simple approach performs very well on a diverse set of continual learning benchmarks, outperforming generative replay and other existing baselines that do not store data.
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。
