Abstract
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
Abstract (translated)
旨在通过参数软最大分类器的指标空间利用率，最近的研究建议用非参数分类器来代替它。尽管非参数分类器可以提供更好的指标空间利用率，但它引入了捕捉类间关系的问题。在先前的非参数分类器中，共同的特点是训练期间将标签分配给原型（即每个原型在训练过程中始终代表一个类别）。与之前的工作不同，我们提出了一个简单而有效的优化方法来优化在训练期间分配给每个原型的类别（标签-原型分配映射）。为了实现这一目标，我们将问题转化为网络参数和标签-原型分配映射的二维优化目标。我们通过梯度下降和Bipartite匹配来求解这个优化问题。我们通过使用不同骨干网络架构对平衡和长尾分类问题进行实验，证明了所提出方法的优越性。特别是，我们的方法在CIFAR-100上的准确率比其竞争对手高1.22%，而在ImageNet-200上的准确率比其竞争对手高2.15%。代码：https:// this URL
URL
https://arxiv.org/abs/2403.16937
