Streaming learning with Move-to-Data approach for image classification

In Deep Neural Network training, the availability of a large amount of representative training data is the sine qua non-condition for a good generalization capacity of the model. In many real-world applications, data is not available at a glance, but coming on the fly. If a pre-trained model is fine-tuned on the new data, then catastrophic forgetting happens mostly. Incremental learning mechanisms propose ways to overcome catastrophic forgetting. Streaming learning is a type of incremental learning where models learn from new data instances as soon as they become available in a single training pass. In this work, we conduct an experimental study, on a large dataset, of an incremental/streaming learning method Move-to-Data we previously proposed, and propose an updated approach by ”re-targeting” with gradient descent which is faster than the popular streaming learning method ExStream. The method achieves better performances and computational efficiency compared to ExStream. Move-to-Data with gradient is on average 3.5 times faster than ExStream and has a similar accuracy, with 0.5% improvement compared to ExStream.