Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification

Supervised learning using deep convolutional neural network has shown its promise in large-scale image classification task. As a building block, it is now well positioned to be part of a larger system that tackles real-life multimedia tasks. An unresolved issue is that such model is trained on a static snapshot of data. Instead, this paper positions the training as a continuous learning process as new classes of data arrive. A system with such capability is useful in practical scenarios, as it gradually expands its capacity to predict increasing number of new classes. It is also our attempt to address the more fundamental issue: a good learning system must deal with new knowledge that it is exposed to, much as how human do.