# A New Loss Function for CNN Classifier Based on Pre-defined Evenly-Distributed Class Centroids

arXiv: https://arxiv.org/abs/1904.06008 · HTML: https://mlsanity.com/paper/1904.06008/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [Score-oriented loss (SOL) functions](https://mlsanity.com/paper/2103.15522/) (2021)
- [Generation and frame characteristics of predefined evenly-distributed class centroids for pattern classification](https://mlsanity.com/paper/2105.00401/) (2021)
- [Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss](https://mlsanity.com/paper/2204.04665/) (2022)
- [A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics](https://mlsanity.com/paper/2305.13472/) (2023)
- [Convolution Neural Network Hyperparameter Optimization Using Simplified Swarm Optimization](https://mlsanity.com/paper/2103.03995/) (2021)
- [Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy](https://mlsanity.com/paper/2104.05450/) (2021)
- [A Softmax-free Loss Function Based on Predefined Optimal-distribution of Latent Features for Deep Learning Classifier](https://mlsanity.com/paper/2111.15449/) (2021)
- [Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layer](https://mlsanity.com/paper/2405.01662/) (2024)
- [Class Anchor Margin Loss for Content-Based Image Retrieval](https://mlsanity.com/paper/2306.00630/) (2023)
- [Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization](https://mlsanity.com/paper/2504.19370/) (2025)

## Abstract (arXiv)

With the development of convolutional neural networks (CNNs) in recent years, the network structure has become more and more complex and varied, and has achieved very good results in pattern recognition, image classification, object detection and tracking. For CNNs used for image classification, in addition to the network structure, more and more research is now focusing on the improvement of the loss function, so as to enlarge the inter-class feature differences, and reduce the intra-class feature variations as soon as possible. Besides the traditional Softmax, typical loss functions include L-Softmax, AM-Softmax, ArcFace, and Center loss, etc. Based on the concept of predefined evenly-distributed class centroids (PEDCC) in CSAE network, this paper proposes a PEDCC-based loss function called PEDCC-Loss, which can make the inter-class distance maximal and intra-class distance small enough in hidden feature space. Multiple experiments on image classification and face recognition have proved that our method achieve the best recognition accuracy, and network training is stable and easy to converge. Code is available in https://github.com/ZLeopard/PEDCC-Loss