Learning without Forgetting

When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

1 Introduction

Many practical vision applications require learning new visual capabilities while maintaining performance on existing ones. For example, a robot may be delivered to someone's house with a set of default object recognition capabilities, but new site-specific object models need to be added. Or for construction safety, a system can identify whether a worker is wearing a safety vest or hard hat, but a superintendent may wish to add the ability to detect improper footware. Ideally, the new tasks could be learned while sharing parameters from old ones, without suffering from Catastrophic Forgetting [1], [2] (degrading performance on old tasks) or having access to the old training data. Legacy data may be unrecorded, proprietary, or simply too cumbersome to use in training a new task. This problem is similar in spirit to transfer, multitask, and lifelong learning.