Benchmarking Catastrophic Forgetting in Neural Networks
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Catastrophic Forgetting is a behavior seen in artificial neural networks (ANNs) when new information overwrites old in such a way that the old information is no longer usable. Since this happens very rapidly in ANNs, it leads to both major practical problems and problems using the artificial networks as models for the human brain. In this thesis I will approach the problem from the practical viewpoint and attempt to provide rules, guidelines, datasets and analysis methods that can aid researchers better analyze new ANN models in terms of catastrophic forgetting and thus lead to better solutions. I suggest two methods of analysis that measure the overlap between input patterns in the input space. I will show strong indications that these measurements can predict if a back-propagation network will retain information better or worse. I will also provide source code implemented in Matlab for analyzing datasets, both with the new suggested measurements and other existing ones, and for running experiments measuring the catastrophic forgetting.