基于判别性与可比性单类分类器的无样本类增量学习
Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
AI总结：
针对无样本类增量学习中并行单类分类器判别性与可比性弱的问题，提出DisCOIL框架，利用变分自编码器生成伪样本并进行对比训练与合页重建损失，在多个数据集上实现了最先进的性能。
摘要：
无样本类增量学习要求分类模型在不保留任何旧样本的情况下增量地学习新类知识。最近，基于并行单类分类器（POC）的框架引起了广泛关注，该框架为每个类别独立训练一个单类分类器（OCC），因为它能自然地避免灾难性遗忘。然而，由于对不同OCC采用独立训练策略，POC存在判别性和可比性较弱的问题。为应对这一挑战，我们提出了一种新框架，名为用于增量学习的判别性与可比性单类分类器（DisCOIL）。DisCOIL遵循POC的基本原理，但它采用变分自编码器（VAE）代替其他成熟的单类分类器（如deep SVDD），因为训练好的VAE不仅能识别输入样本属于某一类的概率，还能生成该类的伪样本来辅助学习新任务。凭借这一优势，DisCOIL在训练新类VAE时与旧类VAE进行对比，迫使新类VAE对新类样本重建得更好，而对旧类伪样本重建得更差，从而增强可比性。此外，DisCOIL引入了合页重建损失以确保判别性。我们在MNIST、CIFAR10和Tiny-ImageNet上对方法进行了广泛评估。实验结果表明，DisCOIL达到了最先进的性能。
英文摘要：
The exemplar-free class incremental learning requires classification models to learn new class knowledge incrementally without retaining any old samples. Recently, the framework based on parallel one-class classifiers (POC), which trains a one-class classifier (OCC) independently for each category, has attracted extensive attention, since it can naturally avoid catastrophic forgetting. POC, however, suffers from weak discriminability and comparability due to its independent training strategy for different OOCs. To meet this challenge, we propose a new framework, named Discriminative and Comparable One-class classifiers for Incremental Learning (DisCOIL). DisCOIL follows the basic principle of POC, but it adopts variational auto-encoders (VAE) instead of other well-established one-class classifiers (e.g. deep SVDD), because a trained VAE can not only identify the probability of an input sample belonging to a class but also generate pseudo samples of the class to assist in learning new tasks. With this advantage, DisCOIL trains a new-class VAE in contrast with the old-class VAEs, which forces the new-class VAE to reconstruct better for new-class samples but worse for the old-class pseudo samples, thus enhancing the comparability. Furthermore, DisCOIL introduces a hinge reconstruction loss to ensure the discriminability. We evaluate our method extensively on MNIST, CIFAR10, and Tiny-ImageNet. The experimental results show that DisCOIL achieves state-of-the-art performance.
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