This report introduces a training and recognition scheme, in which classification is realized via class-wise discerning. Trained with datasets whose labels are randomly shuffled except for one class of interest, a neural network learns class-wise parameter values, and remolds itself from a feature sorter into feature filters, each of which discerns objects belonging to one of the classes only. Classification of an input can be inferred from the maximum response of the filters. A multiple check with multiple versions of filters can diminish fluctuation and yields better performance. This scheme of discerning, maximum response and multiple check is a method of general viability to improve performance of feedforward networks, and the filter training itself is a promising feature abstraction procedure. In contrast to the direct sorting, the scheme mimics the classification process mediated by a series of one component picking.
The proposed scheme mimics the classification process mediated by a series of one component picking. A method to increase accuracy of deep-nets on multi-class classification tasks seemingly by a reduction of multi-class to binary classification. A novel classification procedure of discerning, maxmum response, and multiple check to improve accuracy of mediocre networks and enhance feedforward networks.