Unsupervised learning to detect loops using deep neural networks for visual SLAM system

2015 ◽  
Vol 41 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Xiang Gao ◽  
Tao Zhang
Keyword(s):  
Neural Networks ◽  
Unsupervised Learning ◽  
Deep Neural Networks ◽  
Visual Slam

scholarly journals Unsupervised Representation Learning by Predicting Random Distances

2020 ◽  
Author(s):  
Hu Wang ◽  
Guansong Pang ◽  
Chunhua Shen ◽  
Congbo Ma
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Unsupervised Learning ◽  
Large Scale ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Representation Learning ◽  
Great Success ◽  
Learning Tasks ◽  
Real World Datasets

Deep neural networks have gained great success in a broad range of tasks due to its remarkable capability to learn semantically rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption in unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications to critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. Random mapping is a theoretically proven approach to obtain approximately preserved distances. To well predict these distances, the representation learner is optimised to learn genuine class structures that are implicitly embedded in the randomly projected space. Empirical results on 19 real-world datasets show that our learned representations substantially outperform a few state-of-the-art methods for both anomaly detection and clustering tasks. Code is available at: \url{https://git.io/RDP}

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation
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