On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning.

Plastic neural networks have the ability to adapt to new tasks. However, in a continual learning setting, the configuration of parameters learned in previous tasks can severely reduce the adaptability to future tasks. In particular, we show that, when using weight decay, weights in successive layers of a deep network may become "mutually frozen". This has a double effect: on the one hand, it makes the network updates more invariant to nuisance factors, providing a useful bias for future tasks. On the other hand, it can prevent the network from learning new tasks that require significantly different features. In this context, we find that the local input sensitivity of a deep model is correlated with its ability to adapt, thus leading to an intriguing trade-off between adaptability and invariance when training a deep model more than once. We then show that a simple intervention that "resets" the mutually frozen connections can improve transfer learning on a variety of visual classification tasks. The efficacy of "resetting" itself depends on the size of the target dataset and the difference of the pre-training and target domains, allowing us to achieve state-of-the-art results on some datasets.