Deep online classification using pseudo-generative models

In this work we propose a new deep learning based approach for online classification on streams of high-dimensional data. While requiring very little historical data storage, our approach is able to alleviate catastrophic forgetting in the scenario of continual learning with no assumption on the stationarity of the data in the stream. To make up for the absence of historical data, we propose a new generative autoencoder endowed with an auxiliary loss function that ensures fast task-sensitive convergence. To evaluate our approach we perform experiments on two well-known image datasets, MNIST and LSUN, in a continuous streaming mode. We extend the experiments to a large multi-class synthetic dataset that allows to check the performance of our method in more challenging settings with up to 1000 distinct classes. Our approach is able to perform classification on dynamic data streams with an accuracy close to the results obtained in the offline classification setup where all the data are available for the full duration of training. In addition, we demonstrate the ability of our method to adapt to unseen data classes and new instances of already known data categories, while avoiding catastrophic forgetting of previously acquired knowledge.