Subaerial beach profiles classification: An unsupervised deep learning approach

2021 ◽  
pp. 104508
Author(s):  
Amin Riazi ◽  
Peter A. Slovinsky
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Unsupervised Deep Learning

scholarly journals Unsupervised Deep Learning via Affinity Diffusion

2020 ◽  
Vol 34 (07) ◽  
pp. 11029-11036
Author(s):  
Jiabo Huang ◽  
Qi Dong ◽  
Shaogang Gong ◽  
Xiatian Zhu
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
General Purpose ◽  
Training Data ◽  
Learning Approach ◽  
Model Learning ◽  
Feature Representations ◽  
Discriminative Feature ◽  
Training Samples ◽  
Unsupervised Deep Learning

Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vision tasks. However, they usually rely on supervised model learning with the need for massive labelled training data, limiting dramatically their usability and deployability in real-world scenarios without any labelling budget. In this work, we introduce a general-purpose unsupervised deep learning approach to deriving discriminative feature representations. It is based on self-discovering semantically consistent groups of unlabelled training samples with the same class concepts through a progressive affinity diffusion process. Extensive experiments on object image classification and clustering show the performance superiority of the proposed method over the state-of-the-art unsupervised learning models using six common image recognition benchmarks including MNIST, SVHN, STL10, CIFAR10, CIFAR100 and ImageNet.

An unsupervised deep learning approach for 4DCT lung deformable image registration

2020 ◽  
Author(s):  
Yabo Fu ◽  
Yang Lei ◽  
Tonghe Wang ◽  
Kristin Higgins ◽  
Jeffrey D. Bradley ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Learning Approach ◽  
Unsupervised Deep Learning

scholarly journals Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach

2019 ◽  
pp. 47-55 ◽  
Author(s):  
Amal Lahiani ◽  
Jacob Gildenblat ◽  
Irina Klaman ◽  
Shadi Albarqouni ◽  
Nassir Navab ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Pathology ◽  
Learning Approach ◽  
Unsupervised Deep Learning

De-noising Receiver function data using the unsupervised deep learning approach

2021 ◽  
Author(s):  
Bijayananda Dalai ◽  
Prakash Kumar ◽  
Uppala Srinu ◽  
Mrinal K Sen
Keyword(s):  
Deep Learning ◽  
Receiver Function ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Synthetic Data ◽  
Receiver Functions ◽  
Learning Approach ◽  
Converted Wave ◽  
Wave Data ◽  
Unsupervised Deep Learning

Summary The converted wave data (P-to-s or S-to-p), traditionally termed as receiver functions, are often contaminated with noise of different origin that may lead to the erroneous identification of phases and thus influence the interpretations. Here we utilize an unsupervised deep learning approach called Patchunet to de-noise the converted wave data. We divide the input data into several patches, which are input to the encoder and decoder network to extract some meaningful features. The method de-noises an image patch-by-patch and utilizes the redundant information on similar patches to obtain the final de-noised results. The method is first tested on a suite of synthetic data contaminated with various amount of Gaussian and realistic noise and then on the observed data from three permanent seismic stations: HYB (Hyderabad, India), LBTB (Lobatse, Botswana, South Africa), COR (Corvallis, Oregon, USA). The method works very well even when the signal-to-noise ratio is poor or with the presence of spike noise and deconvolution artifacts. The field data demonstrate the effectiveness of the method for attenuating the random noise especially for the mantle phases, which show significant improvements over conventional receiver function based images.

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