Semi-Supervised Approach Using Nearest Neighbors Clustering and Deep Learning

Abstract

: The field of Deep Learning is in constant evolution, with new techniques and applications being developed by the day. Of those techniques, semi-supervised deep learning have promising results, especially in combination with the standard Convolutional Neural Network (CNN) architectures. CNNs attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, labeling sufficiently large training data sets requires human involvement, which is expensive and time consuming. In semi-supervised learning there is not only a set of labeled samples ( L ), but also a set of unlabeled samples ( U ), which is generally greater than the first ( U > L ). This paper presents a semi-supervised model using a CNN supported by a Multilayer Perceprton (MLP) network, and a clustering process by k Nearest Labeled Neighbors. The results showed that the proposed model solves the semi-supervised learning problem over different scenarios.