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Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks

2021-08-26 15:14:38
Bin Liu

Abstract

This paper is concerned with image classification based on deep convolutional neural networks (CNNs). The focus is centered around the following question: given a set of candidate CNN models, how to select the right one that has the best generalization property for the current task? Present model selection methods require access to a batch of labeled data for defining a performance metric, such as the cross-entropy loss, the classification error rate, the negative log-likelihood, and so on. In many practical cases, however, labeled data are not available in time as labeling itself is a time-consuming and expensive task. To this end, this paper presents an approach to CNN model selection using only unlabeled data. This method is developed based on a principle termed consistent relative confidence (CRC). The effectiveness and efficiency of the presented method are demonstrated by extensive experimental studies based on datasets MNIST and FasionMNIST.

Abstract (translated)

URL

https://arxiv.org/abs/2108.11845

PDF

https://arxiv.org/pdf/2108.11845.pdf


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