---
title: "Improving CNN Performance with Min-Max Objective"
authors:
  - "Weiwei Shi"
  - "Yihong Gong"
  - "Jinjun Wang"
doi: ""
url: "https://ijcai.org/proceedings/2016/286"
pdf: "https://ijcai.org/proceedings/2016/0286.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2004-2009"
track: ""
keywords:
---

# Improving CNN Performance with Min-Max Objective

Weiwei Shi, Yihong Gong, Jinjun Wang

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2004-2009.

## Abstract

In this paper, we propose a novel method to improve object recognition accuracies of convolutional neural networks (CNNs) by embedding the proposed Min-Max objective into a high layer of the models during the training process. The Min-Max objective explicitly enforces the learned object feature maps to have the minimum compactness for each object manifold and the maximum margin between different object manifolds. The Min-Max objective can be universally applied to different CNN models with negligible additional computation cost. Experiments with shallow and deep models on four benchmark datasets including CIFAR-10, CIFAR-100, SVHN and MNIST demonstrate that CNN models trained with the Min-Max objective achieve remarkable performance improvements compared to the corresponding baseline models.