Rehearsal-based Continual Learning (CL) has been intensely investigated in
Deep Neural Networks (DNNs). However, its application in Spiking Neural
Networks (SNNs) has not been explored in depth. In this paper we introduce the
first memory-efficient implementation of Latent Replay (LR)-based CL for SNNs,
designed to seamlessly integrate with resource-constrained devices. LRs combine
new samples with latent representations of previously learned data, to mitigate
forgetting. Experiments on the Heidelberg SHD dataset with Sample and
Class-Incremental tasks reach a Top-1 accuracy of 92.5% and 92%, respectively,
without forgetting the previously learned information. Furthermore, we minimize
the LRs' requirements by applying a time-domain compression, reducing by two
orders of magnitude their memory requirement, with respect to a naive rehearsal
setup, with a maximum accuracy drop of 4%. On a Multi-Class-Incremental task,
our SNN learns 10 new classes from an initial set of 10, reaching a Top-1
accuracy of 78.4% on the full test set.

基于回顾的持续学习在深度神经网络中被广泛研究，然而在脉冲神经网络中的应用尚未深入探索。本文介绍了第一种用于脉冲神经网络的内存高效实现的潜在重放（LR）- 基于回顾持续学习，旨在与资源受限设备无缝集成。实验结果表明，在 Heidelberg SHD 数据集上进行的示例和类增量任务中，Top-1 准确率分别达到 92.5％和 92％，而不会忘记先前学到的信息。此外，通过应用时间域压缩，我们将 LR 的存储要求减少了两个数量级，并且与简单的回顾设置相比，最大准确率下降仅为 4％。在多类增量任务中，我们的脉冲神经网络从初始的 10 个类别学习了 10 个新类别，最终在完整测试集上达到了 78.4％的 Top-1 准确率。

基于压缩潜在重放的轻量级脉冲神经网络的持续学习

Compressed Latent Replays for Lightweight Continual Learning on Spiking  Neural Networks

Training deep neural networks at the edge on light computational devices,
embedded systems and robotic platforms is nowadays very challenging. Continual
learning techniques, where complex models are incrementally trained on small
batches of new data, can make the learning problem tractable even for CPU-only
embedded devices enabling remarkable levels of adaptiveness and autonomy.
However, a number of practical problems need to be solved: catastrophic
forgetting before anything else. In this paper we introduce an original
technique named "Latent Replay" where, instead of storing a portion of past
data in the input space, we store activations volumes at some intermediate
layer. This can significantly reduce the computation and storage required by
native rehearsal. To keep the representation stable and the stored activations
valid we propose to slow-down learning at all the layers below the latent
replay one, leaving the layers above free to learn at full pace. In our
experiments we show that Latent Replay, combined with existing continual
learning techniques, achieves state-of-the-art performance on complex video
benchmarks such as CORe50 NICv2 (with nearly 400 small and highly non-i.i.d.
batches) and OpenLORIS. Finally, we demonstrate the feasibility of nearly
real-time continual learning on the edge through the deployment of the proposed
technique on a smartphone device.

本文提出了一种称为 “隐式回放” 的技术，该技术可以在边缘计算和嵌入式设备上有效地训练复杂的神经网络模型，并利用连续学习技术实现了较高的自适应性和自主性。我们的实验表明，结合现有的连续学习技术，隐式回放在复杂视频基准测试方面取得了最先进的性能，并且在智能手机上部署该技术可以实现几乎实时的连续学习。