# Catastrophic forgetting: still a problem for DNNs

arXiv: https://arxiv.org/abs/1905.08077 · HTML: https://mlsanity.com/paper/1905.08077/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 7 of 7)

- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
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- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Geodesics, Non-linearities and the Archive of Novelty Search](https://mlsanity.com/paper/2205.03162/) (2022)
- [Deep Learning Training Procedure Augmentations](https://mlsanity.com/paper/2211.14395/) (2022)
- [Optimisation Is Not What You Need](https://mlsanity.com/paper/2507.03045/) (2025)
- [Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP](https://mlsanity.com/paper/2509.11270/) (2025)

## Abstract (arXiv)

We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes. Catastrophic forgetting (CF) behavior is measured using a new evaluation procedure that aims at an application-oriented view of incremental learning. In particular, it imposes that model selection must be performed on the initial dataset alone, as well as demanding that retraining control be performed only using the retraining dataset, as initial dataset is usually too large to be kept. Experiments are conducted on class-incremental problems derived from MNIST, using a variety of different DNN models, some of them recently proposed to avoid catastrophic forgetting. When comparing our new evaluation procedure to previous approaches for assessing CF, we find their findings are completely negated, and that none of the tested methods can avoid CF in all experiments. This stresses the importance of a realistic empirical measurement procedure for catastrophic forgetting, and the need for further research in incremental learning for DNNs.