Incremental class learning involves sequentially learning classes in bursts of examples from the same class. This violates the assumptions that underlie methods for training standard deep neural networks, and will cause them to suffer from catastrophic forgetting. Arguably, the best method for incremental class learning is iCaRL, but it requires storing training examples for each class, making it challenging to scale. Here, we propose FearNet for incremental class learning. FearNet is a generative model that does not store previous examples, making it memory efficient. FearNet uses a brain-inspired dual-memory system in which new memories are consolidated from a network for recent memories inspired by the mammalian hippocampal complex to a network for long-term storage inspired by medial prefrontal cortex. Memory consolidation is inspired by mechanisms that occur during sleep. FearNet also uses a module inspired by the basolateral amygdala for determining which memory system to use for recall. FearNet achieves state-of-the-art performance at incremental class learning on image (CIFAR-100, CUB-200) and audio classification (AudioSet) benchmarks.

本论文提出了FearNet作为一种新的增量类别学习方法，FearNet是一种不需要存储先前样本的生成模型，使用神经网络实现一个双重记忆系统，通过处于不同阶段的记忆体以及受哺乳动物海马和额叶前皮层影响的新和长时记忆的网络之间转换以提高记忆效率和防止灾难性遗忘。FearNet在图像（CIFAR-100，CUB-200）和音频（AudioSet）分类基准测试上均取得了最先进的性能。

FearNet: 增量学习的类脑模型