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

• URL: http://arxiv.org/abs/2108.11845v1
• Date: Thu, 26 Aug 2021 15:14:38 GMT
• Title: Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks
• Authors: Bin Liu
• Abstract summary: This paper presents an approach to CNN model selection using only unlabeled data. The effectiveness and efficiency of the presented method are demonstrated by extensive experimental studies based on datasets MNIST and FasionMNIST.
• Score: 4.497097230665825
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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