[ContinualAI Reading group]: "ACAE-REMIND for Online Continual Learning with Compressed Feature Replay"

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Keiland Cooper

Jun 9, 2021, 11:51:43 AM6/9/21
to Continual Learning & AI News

Hi All,

This Friday 06-11-2021, 5.30pm CEST, for the ContinualAI Reading Group, Kai Wang  will present the paper:

Abstract: 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.

The event will be moderated by: Vincenzo Lomonaco

- Eventbrite event (to save it in you calendar and get reminders): [ Click Here
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- YouTube recordings of the previous sessions: https://www.youtube.com/c/ContinualAI

Feel free to share this email to anyone interested and invite them to subscribe this mailing-list here: https://groups.google.com/g/continualai 

Please also contact me if you want to speak at one of the next sessions!

Looking forward to seeing you all there!

All the best,
Keiland Cooper

University of California
ContinualAI Co-founding Board Member
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