# An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks

**Authors:** Ian J. Goodfellow, Mehdi Mirza, Xia Da, Aaron Courville, Yoshua Bengio  
**arXiv:** [1312.6211](https://arxiv.org/abs/1312.6211)  
**Date:** 2013-12-21  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/1312.6211v1/

## Abstract

Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget'' how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting.