5 research outputs found
Does Continual Learning = Catastrophic Forgetting?
Continual learning is known for suffering from catastrophic forgetting, a
phenomenon where earlier learned concepts are forgotten at the expense of more
recent samples. In this work, we challenge the assumption that continual
learning is inevitably associated with catastrophic forgetting by presenting a
set of tasks that surprisingly do not suffer from catastrophic forgetting when
learned continually. We provide evidence that these reconstruction-type tasks
exhibit positive forward transfer and that single-view 3D shape reconstruction
improves the performance on learned and novel categories over time. We provide
the novel analysis of knowledge transfer ability by looking at the output
distribution shift across sequential learning tasks. Finally, we show that the
robustness of these tasks leads to the potential of having a proxy
representation learning task for continual classification. The codebase,
dataset, and pre-trained models released with this article can be found at
https://github.com/rehg-lab/CLRec
Model Zoo: A Growing "Brain" That Learns Continually
This paper argues that continual learning methods can benefit by splitting
the capacity of the learner across multiple models. We use statistical learning
theory and experimental analysis to show how multiple tasks can interact with
each other in a non-trivial fashion when a single model is trained on them. The
generalization error on a particular task can improve when it is trained with
synergistic tasks, but can also deteriorate when trained with competing tasks.
This theory motivates our method named Model Zoo which, inspired from the
boosting literature, grows an ensemble of small models, each of which is
trained during one episode of continual learning. We demonstrate that Model Zoo
obtains large gains in accuracy on a variety of continual learning benchmark
problems