A Softmax-free Loss Function Based on Predefined Optimal-distribution of Latent Features for CNN Classifier

• URL: http://arxiv.org/abs/2111.15449v1
• Date: Thu, 25 Nov 2021 06:01:53 GMT
• Title: A Softmax-free Loss Function Based on Predefined Optimal-distribution of Latent Features for CNN Classifier
• Authors: Qiuyu Zhu, Xuewen Zu
• Abstract summary: This article proposes a Softmax-free loss function (POD Loss) based on predefined optimal-distribution of latent features. The loss function only restricts the latent features of the samples, including the cosine distance between the latent feature vector of the sample and the center of the predefined evenly-distributed class. Compared with the commonly used Softmax Loss and the typical Softmax related AM-Softmax Loss, COT-Loss and PEDCC-Loss, experiments on several commonly used datasets on a typical network show that POD Loss is always better and easier to converge.
• Score: 4.7210697296108926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of pattern classification, the training of convolutional neural network classifiers is mostly end-to-end learning, and the loss function is the constraint on the final output (posterior probability) of the network, so the existence of Softmax is essential. In the case of end-to-end learning, there is usually no effective loss function that completely relies on the features of the middle layer to restrict learning, resulting in the distribution of sample latent features is not optimal, so there is still room for improvement in classification accuracy. Based on the concept of Predefined Evenly-Distributed Class Centroids (PEDCC), this article proposes a Softmax-free loss function (POD Loss) based on predefined optimal-distribution of latent features. The loss function only restricts the latent features of the samples, including the cosine distance between the latent feature vector of the sample and the center of the predefined evenly-distributed class, and the correlation between the latent features of the samples. Finally, cosine distance is used for classification. Compared with the commonly used Softmax Loss and the typical Softmax related AM-Softmax Loss, COT-Loss and PEDCC-Loss, experiments on several commonly used datasets on a typical network show that the classification performance of POD Loss is always better and easier to converge. Code is available in https://github.com/TianYuZu/POD-Loss.

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