Semi-Supervised Approach Using Nearest Neighbors Clustering and Deep Learning

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.