Aiming to enhance the utilization of metric space by the parametric softmax
classifier, recent studies suggest replacing it with a non-parametric
alternative. Although a non-parametric classifier may provide better metric
space utilization, it introduces the challenge of capturing inter-class
relationships. A shared characteristic among prior non-parametric classifiers
is the static assignment of labels to prototypes during the training, ie, each
prototype consistently represents a class throughout the training course.
Orthogonal to previous works, we present a simple yet effective method to
optimize the category assigned to each prototype (label-to-prototype
assignment) during the training. To this aim, we formalize the problem as a
two-step optimization objective over network parameters and label-to-prototype
assignment mapping. We solve this optimization using a sequential combination
of gradient descent and Bipartide matching. We demonstrate the benefits of the
proposed approach by conducting experiments on balanced and long-tail
classification problems using different backbone network architectures. In
particular, our method outperforms its competitors by 1.22\% accuracy on
CIFAR-100, and 2.15\% on ImageNet-200 using a metric space dimension half of
the size of its competitors. Code:
this https URL

通过优化网络参数和原型标签之间的映射关系，我们提出了一种在训练过程中优化每个原型所分配的类别的简单而有效的方法。