Sequential Analysis with Specified Confidence Level and Adaptive Convolutional Neural Networks in Image Recognition

Abstract

In this paper the problem of high computational complexity of deep convolutional nets in image recognition is considered. An existing framework of adaptive neural networks is extended by appending the separate classifier to intermediate layers. The hierarchical representations of the input image are sequentially analyzed. If the first classifier returns rather high confidence score, the inference process will be terminated. Otherwise, the inference to the next intermediate layer with attached classifier is continued until the reliable solution is obtained or the penultimate layer is reached. The thresholds for classifier scores at each layer are automatically chosen based on the Benjamini-Hochberg multiple comparisons for a specified confidence level. Experimental study for both pre-trained and fine-tuned deep convolutional neural networks demonstrates that the proposed approach reduces the running time by up to 1.7 times without significant accuracy degradation. Moreover, the larger is the training sample, the more noticeable is the gain in performance.