An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks
Authors: Yoshua Bengio, Mehdi Mirza, Ian Goodfellow, Aaron Courville, Xia Da
ICLR 2014

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.


Peer review:
[Review]:
The paper 'An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks' investigates the robustness of 'modern' feed-forward networks trained on dropout and a variety of activation functions to task-switching during training. Biological neural networks have the property that after being trained for a task A, and then trained for a different task B, they can be re-trained for A more quickly than networks that have never mastered A before.  This paper investigates the capacity of various sorts of feed-forward neural networks to exhibit this property.                                                                  
 
 
Novelty:                                                                                                                            
 
The authors state that the issue was 'well-studied in the past' but provide no references or summary of that past work. I am not familiar with that literature.

Quality:                                                                                                                            

The paper was a joy to read. As a reviewer, I want to thank the authors for taking the time to write so clearly.                    

The large parenthetical remark before section 4.1 should probably be the caption on the page with Figures 1 and 3.


Pro:

The issue of how deep networks might transfer patterns learned from one data set to another is important and topical.

The experiments are presented clearly and reveal an interesting new property of dropout training.


Con:

The suggestion that the first layer of a neural network is not rewired in response to a remapping of MNIST pixels, but rather upper layers handle the new task is counter-intuitive and intriguing. The paper would be stronger if the authors tested this hypothesis.

Comments:
 
Experiments intro: a surprising omission was that prior to dropout L2 and L1 weight regularization
 
Figures 1 and 3: A better caption would help these intriguing figures to stand on their own. Is a lower-envelope on a scatter plot of randomly drawn models being drawn here? Is there a modelling reason that motivates connecting the dots, or is it simply a visual aide? Did you consider leaving all of the scatter-plots on the axes (though it might be too busy)?                                
 
I think the experimental protocol used here is not quite the same as the one used to label catastrophic forgetting in neural networks: there, task A is trained to mastery, then B, then A again. The question is how fast someone can go from A to B and back, without regard for how good they are at A or B in absolute terms. Here, an early stopping criterion was used during B-training so that the question of how fast A degrades may be confounded with the networks' raw ability to do tasks A and B.