scholarly journals Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1593
Author(s):  
Hongying Liu ◽  
Ruyi Luo ◽  
Fanhua Shang ◽  
Xuechun Meng ◽  
Shuiping Gou ◽  
...  

Recently, classification methods based on deep learning have attained sound results for the classification of Polarimetric synthetic aperture radar (PolSAR) data. However, they generally require a great deal of labeled data to train their models, which limits their potential real-world applications. This paper proposes a novel semi-supervised deep metric learning network (SSDMLN) for feature learning and classification of PolSAR data. Inspired by distance metric learning, we construct a network, which transforms the linear mapping of metric learning into the non-linear projection in the layer-by-layer learning. With the prior knowledge of the sample categories, the network also learns a distance metric under which all pairs of similarly labeled samples are closer and dissimilar samples have larger relative distances. Moreover, we introduce a new manifold regularization to reduce the distance between neighboring samples since they are more likely to be homogeneous. The categorizing is achieved by using a simple classifier. Several experiments on both synthetic and real-world PolSAR data from different sensors are conducted and they demonstrate the effectiveness of SSDMLN with limited labeled samples, and SSDMLN is superior to state-of-the-art methods.

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1066 ◽  
Author(s):  
Kaya ◽  
Bilge

Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers' attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.


2020 ◽  
Vol 17 (9) ◽  
pp. 4125-4130
Author(s):  
Gaurav Karkal ◽  
K. Dhanush Reddy ◽  
Kaushik Singh ◽  
Nikith Hosangadi ◽  
Annapurna P. Patil

Standard deep learning in the context of facial recognition involves inputting a single image and outputting a label for that image. Deep metric learning distinguishes itself by outputting a real valued feature vector instead of a single label. The usage of deep metric learning has revolutionised facial recognition, making it very accurate and reliable. This paper exhibits the accuracy and reliability of the facial recognition model using deep metric learning in the application of an automated attendance system. The paper presents a non-intrusive attendance system which uses the described neural network to recognize faces and record attendance. The system uses the pre-trained neural network to generate embeddings for faces, using a method known as the triple training step, which is described in the paper. These embeddings are generated from a collection of photos per person. After the embeddings are generated, the system is ready to perform facial recognition on sample photos. CNN is used for facial detection in the sample group photos. Once the faces are detected, a KNN classifier is used for recognizing faces. Finally after the faces are recognized, the attendance for each recognized student is marked in the database. Thus, the whole process of attendance was automated without the requirement of human interaction.


2020 ◽  
Vol 34 (04) ◽  
pp. 4683-4690 ◽  
Author(s):  
Shuheng Li ◽  
Dezhi Hong ◽  
Hongning Wang

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution.


2021 ◽  
Vol 11 (15) ◽  
pp. 6959
Author(s):  
Zaky Dzulfikri ◽  
Pin-Wei Su ◽  
Chih-Yung Huang

Stamping processes remain crucial in manufacturing processes; therefore, diagnosing the condition of stamping tools is critical. One of the challenges in diagnosing stamping tool conditions is that traditionally, the tools need to be visually checked, and the production processes thus need to be halted. With the development of Industry 4.0, intelligent monitoring systems have been developed by using accelerometers and algorithms to diagnose the wear classification of stamping tools. Although several deep learning models such as the convolutional neural network (CNN), auto encoder (AE), and recurrent neural network (RNN) models have demonstrated promising results for classifying complex signals including accelerometer signals, the practicality of those methods are restricted due to the flexibility of adding new classes and low accuracy when faced to low numbers of samples per class. In this study, we applied deep metric learning (DML) methods to overcome these problems. DML involves extracting meaningful features using feature extraction modules to map inputs into embedding features. We compared the probability method, the contrastive method, and a triplet network to determine which method was most suitable for our case. The experimental results revealed that, compared with other models, a triplet network can be more effectively trained with limited training data. The triplet network demonstrated the best test results of the compared methods in the noised test data. Finally, when tested using unseen class, the triplet network and the probability method demonstrated similar results.


Author(s):  
Winston Koh ◽  
Shawn Hoon

Large collections of annotated single-cell RNA sequencing (scRNA-seq) experiments are being generated across different organs, conditions and organisms on different platforms. The diversity, volume and complexity of this aggregated data requires new analysis techniques to extract actionable knowledge. Fundamental to most analysis are key abilities such as: identification of similar cells across different experiments and transferring annotations from an annotated dataset to an unannotated one. There have been many strategies explored in achieving these goals, and they focuses primarily on aligning and re-clustering datasets of interest. In this work, we are interested in exploring the applicability of deep metric learning methods as a form of distance function to capture similarity between cells and facilitate the transfer of cell type annotation for similar cells across different experiments. Toward this aim, we developed MapCell, a few-shot training approach using Siamese Neural Networks (SNNs) to learn a generalizable distance metric that can differentiate between single cell types. Requiring only a small training set, we demonstrated that SNN derived distance metric can perform accurate transfer of annotation across different scRNA-seq platforms, batches, species and also aid in flagging novel cell types.


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