Title: Revisiting Distillation and Incremental Classifier Learning,
Abstract: One of the key differences between the learning mechanism of humans and
Artificial Neural Networks (ANNs) is the ability of humans to learn one task at
a time. ANNs, on the other hand, can only learn multiple tasks simultaneously.
Any attempts at learning new tasks incrementally cause them to completely
forget about previous tasks. This lack of ability to learn incrementally,
called Catastrophic Forgetting, is considered a major hurdle in building a true
AI system. In this paper, our goal is to isolate the truly effective existing
ideas for incremental learning from those that only work under certain
conditions. To this end, we first thoroughly analyze the current state of the
art (iCaRL) method for incremental learning and demonstrate that the good
performance of the system is not because of the reasons presented in the
existing literature. We conclude that the success of iCaRL is primarily due to
knowledge distillation and recognize a key limitation of knowledge
distillation, i.e, it often leads to bias in classifiers. Finally, we propose a
dynamic threshold moving algorithm that is able to successfully remove this
bias. We demonstrate the effectiveness of our algorithm on CIFAR100 and MNIST
datasets showing near-optimal results. Our implementation is available at
this https URL.