Selection of informative elements for the training of a lightweight convolutional neural network classifier in the conditions of a strong imbalance of the training sample
In this paper we consider the task of balancing a training sample in the training of an image classifier based on a convolutional neural network. Аn active scheme of selection (thinning out) of informative elements in the learning process is proposed to teach the lightweight classifier in the conditions of a strong imbalance of the training samples, An experimental verification using the example of the problem of classifying images of handwritten figures and zones of road sign detection shows a stable advantage of the proposed scheme in comparison with random selection.