  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.
