# ACAE-REMIND for Online Continual Learning with Compressed Feature Replay

arXiv: https://arxiv.org/abs/2105.08595 · HTML: https://mlsanity.com/paper/2105.08595/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [Online Continual Learning with Maximally Interfered Retrieval](https://mlsanity.com/paper/1908.04742/) (2019)
- [Expert Gate: Lifelong Learning with a Network of Experts](https://mlsanity.com/paper/1611.06194/) (2016)
- [REMIND Your Neural Network to Prevent Catastrophic Forgetting](https://mlsanity.com/paper/1910.02509/) (2019)
- [Using Hindsight to Anchor Past Knowledge in Continual Learning](https://mlsanity.com/paper/2002.08165/) (2020)
- [Towards Robust Evaluations of Continual Learning](https://mlsanity.com/paper/1805.09733/) (2018)
- [Generative Feature Replay For Class-Incremental Learning](https://mlsanity.com/paper/2004.09199/) (2020)
- [Memory-Efficient Incremental Learning Through Feature Adaptation](https://mlsanity.com/paper/2004.00713/) (2020)
- [Task Agnostic Continual Learning Using Online Variational Bayes](https://mlsanity.com/paper/1803.10123/) (2018)
- [Ternary Feature Masks: zero-forgetting for task-incremental learning](https://mlsanity.com/paper/2001.08714/) (2020)

## Built on by (most cited first, 12 of 12)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [SIESTA: Efficient Online Continual Learning with Sleep](https://mlsanity.com/paper/2303.10725/) (2023)
- [Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2](https://mlsanity.com/paper/2208.05056/) (2022)
- [Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting](https://mlsanity.com/paper/2501.15356/) (2025)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Autoencoder-Based Hybrid Replay for Class-Incremental Learning](https://mlsanity.com/paper/2505.05926/) (2025)
- [System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games](https://mlsanity.com/paper/2212.04603/) (2022)
- [Remind of the Past: Incremental Learning with Analogical Prompts](https://mlsanity.com/paper/2303.13898/) (2023)
- [Hadamard Domain Training with Integers for Class Incremental Quantized Learning](https://mlsanity.com/paper/2310.03675/) (2023)
- [Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification](https://mlsanity.com/paper/2210.01600/) (2022)
- [GaitAdapt: Continual Learning for Evolving Gait Recognition](https://mlsanity.com/paper/2508.03375/) (2025)
- [FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification](https://mlsanity.com/paper/2407.12375/) (2024)

## Abstract (arXiv)

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.