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]:
Summary
This paper explores the behavior of a neural network when the cost function changes from an initial task to a second subsequent task. The authors construct pairs of tasks from standard benchmarks in textual sentiment classification and MNIST digit classification. For constructed pairs of tasks the authors analyze performance of networks trained with four different standard hidden unit activation functions, and dropout regularization. The neural networks used are standard architectures, and there is not anything new from a modeling perspective. The authors conclude that dropout regularization is beneficial on the datasets they evaluate, but there is not a consistent best choice of activation function.

Detailed Review
The problem of catastrophic forgetting seems irrelevant to most of the current research in deep learning, and seems to be a somewhat constructed thought experiment. The paper does not mention a practical task that requires training the same neural network on two problems in sequence. Indeed, I can’t think of such a task, and the authors should significantly rewrite the introduction to make clear why this problem is important. 

The ability of a network to “remember” a previous task is purely an artifact of poor optimization. If the neural network cost function could be better optimized, neural networks, like SVMs, would completely forget any previous task. This is actually a good thing! Neural networks are function approximators and any “remembering” of a previous task exhibits poor performance on fitting the function of interest. This observation again points to doubts as to why catastrophic forgetting is a problem worth studying. 

In terms of experiments, the authors do a relatively good job of exploring possible network architecture choices. My concern with the experimental setup is the contrived nature of the dataset pairs. The pairs of tasks used don’t seem representative of realistic task pairs, but again I can’t think of a task pairs which actually require sequential training. The paper seems incomplete without comparing sequential task training to jointly training on the two tasks. I suspect joint training should perform at least as well.

When discussion dropout, the authors should be careful to not describe it as an alternative to SGD. Dropout is a regularization technique and can be written into the cost function of network training. SGD is an optimization technique used to optimize that cost function, so for example SGD can be replaced with conjugate gradient to train a model with dropout regularization. 

In dropout training some papers have found significant performance impact from the setting of dropout probability. The authors argue that setting the dropout probability to 0.5 is usually best, but it would be nice to have an experimental validation of this claim.

Review Summary
- Forgetting seems like a contrived problem not relevant to current deep learning research
- Evaluation is on toy datasets which do not require sequential task training and could easily be trained jointly
- No comparison is given to jointly training 
+ Reasonable evaluation of various hidden unit activation function choices