DeepAI AI Chat
Log In Sign Up

ACAE-REMIND for Online Continual Learning with Compressed Feature Replay

by   Kai Wang, et al.

Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset.


page 1

page 2

page 3

page 4


Online Continual Learning with Maximally Interfered Retrieval

Continual learning, the setting where a learning agent is faced with a n...

The Effectiveness of Memory Replay in Large Scale Continual Learning

We study continual learning in the large scale setting where tasks in th...

Online Learned Continual Compression with Stacked Quantization Module

We introduce and study the problem of Online Continual Compression, wher...

Improving information retention in large scale online continual learning

Given a stream of data sampled from non-stationary distributions, online...

Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning

We focus on the problem of how to achieve online continual learning unde...

One Pass ImageNet

We present the One Pass ImageNet (OPIN) problem, which aims to study the...

A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays

In the last few years, research and development on Deep Learning models ...