Catastrophic forgetting: still a problem for DNNs
This work highlights a persistent problem for incremental learning in AI, showing that existing solutions are insufficient under realistic conditions, which is incremental but crucial for practical applications.
The study tackled catastrophic forgetting in deep neural networks during class-incremental visual learning by introducing a new evaluation procedure that simulates realistic constraints, such as model selection on initial data only, and found that all tested methods failed to avoid forgetting in experiments on MNIST-derived datasets.
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.