Semi-supervised deep learning based on label propagation in a 2D embedded space
While convolutional neural networks need large labeled sets for training images, expert human supervision of such datasets can be very laborious. Proposed solutions propagate labels from a small set of supervised images to a large set of unsupervised ones to obtain sufficient truly-and-artificially labeled samples to train a deep neural network model. Yet, such solutions need many supervised images for validation. We present a loop in which a deep neural network (VGG-16) is trained from a set with more correctly labeled samples along iterations, created by using t-SNE to project the features of its last max-pooling layer into a 2D embedded space in which labels are propagated using the Optimum-Path Forest semi-supervised classifier. As the labeled set improves along iterations, it improves the features of the neural network. We show that this can significantly improve classification results on test data (using only 1\% to 5\% of supervised samples) of three private challenging datasets and two public ones.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Deep Clustering with Measure Propagation
Deep models have improved state-of-the-art for both supervised and unsupervised learning. For example, deep embedded clustering (DEC) has greatly improved the unsupervised clustering performance, by using stacked autoenc…
ClusteringDeep ClusteringRepresentation LearningShort Text Clustering+2Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning
Lacking supervised data is an issue while training deep neural networks (DNNs), mainly when considering medical and biological data where supervision is expensive. Recently, Embedded Pseudo-Labeling (EPL) addressed this …
Contrastive LearningDeep Transductive Semi-supervised Maximum Margin Clustering
Semi-supervised clustering is an very important topic in machine learning and computer vision. The key challenge of this problem is how to learn a metric, such that the instances sharing the same label are more likely cl…
ClusteringTransductive LearningSemi-supervised classification using a supervised autoencoder for biomedical applications
In this paper we present a new approach to solve semi-supervised classification tasks for biomedical applications, involving a supervised autoencoder network. We create a network architecture that encodes labels into the…
General ClassificationA multi-stage semi-supervised improved deep embedded clustering method for bearing fault diagnosis under the situation of insufficient labeled samples
Although data-driven fault diagnosis methods have been widely applied, massive labeled data are required for model training. However, a difficulty of implementing this in real industries hinders the application of these …
ClusteringDeep ClusteringDiagnosticFault Diagnosis