CLCNet: Rethinking of Ensemble Modeling with Classification Confidence Network

TL;DR

This paper introduces CLCNet, a confidence estimation network that enhances ensemble classification systems by improving accuracy and efficiency, allowing customizable computation and outperforming single models with less resource use.

Contribution

The paper presents a novel confidence network for ensemble modeling that optimizes performance and computational cost, a new approach distinct from traditional ensemble methods.

Findings

System achieves customizable FLOPs per image during inference.

Performance exceeds models of similar structure but different size under same computation.

Enables a new, more efficient ensemble modeling approach.

Abstract

In this paper, we propose a Classification Confidence Network (CLCNet) that can determine whether the classification model classifies input samples correctly. It can take a classification result in the form of vector in any dimension, and return a confidence score as output, which represents the probability of an instance being classified correctly. We can utilize CLCNet in a simple cascade structure system consisting of several SOTA (state-of-the-art) classification models, and our experiments show that the system can achieve the following advantages: 1. The system can customize the average computation requirement (FLOPs) per image while inference. 2. Under the same computation requirement, the performance of the system can exceed any model that has identical structure with the model in the system, but different in size. In fact, this is a new type of ensemble modeling. Like general ensemble modeling, it can achieve higher performance than single classification model, yet our system requires much less computation than general ensemble modeling. We have uploaded our code to a github repository: https://github.com/yaoching0/CLCNet-Rethinking-of-Ensemble-Modeling.