A Memory Replay-Based Continual Learning Utilizing Class Representative and Class Boundary Data

It is known that deep learning models are performing beyond human beings if sufficient size of training data is provided. However, this is usually true only if a model is trained for a single task. If a model is continuously trained on multiple tasks, the model abruptly forgets the previously learned information. To overcome this phenomenon, various continual learning methods have been proposed. In particular, in the class-incremental learning scenario in which no task identification information is given at the inference time, the memory replay method has shown good performances. In general, the memory replay method stores class representative samples to recall previously learned tasks, but this leads to poor performance when a sample can be a representative of multiple classes. Therefore, this paper proposes a method that stores class boundary data as well as class representative data in the memory buffer to improve the performance of the memory replay. For this, a class representative sample is defined as the one of which feature is close to the mean of a certain class, and a class boundary sample is defined to be located between the means of two different classes. The experiments confirm that the proposed method shows higher performance than existing methods, which proves the importance of utilizing class boundary samples in continual learning.