# Autoencoder-Based Incremental Class Learning without Retraining on Old Data

arXiv: https://arxiv.org/abs/1907.07872 · HTML: https://mlsanity.com/paper/1907.07872/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [Learning with Recoverable Forgetting](https://mlsanity.com/paper/2207.08224/) (2022)
- [Partial Network Cloning](https://mlsanity.com/paper/2303.10597/) (2023)
- [Incremental Class Learning using Variational Autoencoders with Similarity Learning](https://mlsanity.com/paper/2110.01303/) (2021)

## Abstract (arXiv)

Incremental class learning, a scenario in continual learning context where classes and their training data are sequentially and disjointedly observed, challenges a problem widely known as catastrophic forgetting. In this work, we propose a novel incremental class learning method that can significantly reduce memory overhead compared to previous approaches. Apart from conventional classification scheme using softmax, our model bases on an autoencoder to extract prototypes for given inputs so that no change in its output unit is required. It stores only the mean of prototypes per class to perform metric-based classification, unlike rehearsal approaches which rely on large memory or generative model. To mitigate catastrophic forgetting, regularization methods are applied on our model when a new task is encountered. We evaluate our method by experimenting on CIFAR-100 and CUB-200-2011 and show that its performance is comparable to the state-of-the-art method with much lower additional memory cost.