Margin Loss Based On Adaptive Metric For Image Recognition

Cross-entropy (CE) loss is one of the most commonly used supervision in image recognition. However, the features trained by CE loss are not discriminative enough. There are many methods using the angular-softmax CE loss to learn angularly discriminative features. But these methods still use manually designed metric, which cannot deal with complicated distribution of high-dimensional features, and there are no appropriate inter-class restraint. Therefore, we propose a novel method to restrain the distribution of features in high-dimensional. We construct trainable centers, and design adaptive metric to express the distance between features. Specifically, we design AdaMetricLoss which can manipulate inter-class distance and intra-class distance simultaneously. We evaluate the effectiveness of AdaMetricLoss on Cifar10 and Cifar100 datasets, and our method shows preferable classification performance. We also visualize the discriminative distribution of features on MNIST, proves the proposed method is more suitable for image recognition.