Analog IC Placement Generation via Neural Networks from Unlabeled Data

Semisupervised Learning for Seismic Monitoring Applications

Seismological Research Letters ◽

10.1785/0220200195 ◽

2020 ◽

Vol 92 (1) ◽

pp. 388-395

Author(s):

Lisa Linville ◽

Dylan Anderson ◽

Joshua Michalenko ◽

Jennifer Galasso ◽

Timothy Draelos

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Seismic Event ◽

Model Performance ◽

Seismic Monitoring ◽

Semisupervised Learning ◽

Unlabeled Data ◽

Hybrid Technique ◽

Source Type ◽

Adversarial Training

Abstract The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. In geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.

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SEMI-SUPERVISED SEMANTIC SEGMENTATION NETWORK VIA LEARNING CONSISTENCY FOR REMOTE SENSING LAND-COVER CLASSIFICATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-609-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 609-615

Author(s):

B. Zhang ◽

Y. Zhang ◽

Y. Li ◽

Y. Wan ◽

F. Wen

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Land Cover ◽

Deep Neural Networks ◽

Semantic Segmentation ◽

Land Cover Classification ◽

Unlabeled Data ◽

Weighted Sum ◽

Affine Transform

Abstract. Current popular deep neural networks for semantic segmentation are almost supervised and highly rely on a large amount of labeled data. However, obtaining a large amount of pixel-level labeled data is time-consuming and laborious. In remote sensing area, this problem is more urgent. To alleviate this problem, we propose a novel semantic segmentation neural network (S4Net) based on semi-supervised learning by using unlabeled data. Our model can learn from unlabeled data by consistency regularization, which enforces the consistency of output under different random transforms and perturbations, such as random affine transform. Thus, the network is trained by the weighted sum of a supervised loss from labeled data and a consistency regularization loss from unlabeled data. The experiments we conducted on DeepGlobe land cover classification challenge dataset verified that our network can make use of unlabeled data to obtain precise results of semantic segmentation and achieve competitive performance when compared to other methods.

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