Results 71 to 80 of about 6,908 (119)
IEEE AccessA fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power module at randomly varied load currents and augmented ...
Noboru Katayama, Rintaro Ishida
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Composite Denoising Autoencoders [PDF]
, 2016 In representation learning, it is often desirable to learn features at different levels of scale. For example, in image data, some edges will span only a few pixels, whereas others will span a large portion of the image. We introduce an unsupervised representation learning method called a composite denoising autoencoder CDA to address this.
Geras, Krzysztof, Sutton, Charles
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The Astronomical JournalWe present a novel method for anomaly detection in solar system object data in preparation for the Legacy Survey of Space and Time. We train a deep autoencoder for anomaly detection and use the learned latent space to search for other interesting objects.
Brian Rogers +4 more
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EnergiesAn On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages.
Fataneh Dabaghi-Zarandi +5 more
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Advanced Flame front Detection in Combustion Processes Using Autoencoder Approach
EnergiesThis research explores the detection of flame front evolution in spark-ignition engines using an innovative neural network, the autoencoder. High-speed camera images from an optical access engine were analyzed under different air excess coefficient λ ...
Federico Ricci, Francesco Mariani
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Oil Spill Classification Using an Autoencoder and Hyperspectral Technology
Journal of Marine Science and EngineeringHyperspectral technology has been playing a leading role in monitoring oil spills in marine environments, which is an issue of international concern. In the case of monitoring oil spills in local areas, hyperspectral technology of small dimensions is the
María Gema Carrasco-García +5 more
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Scheduled denoising autoencoders
, 2014 We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is only slightly corrupted, the ...
Geras, Krzysztof, Sutton, Charles
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Ladder Variational Autoencoders
, 2016 Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models.
Sønderby, Casper Kaae +4 more
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Adversarially Regularized Autoencoders
, 2017 Deep latent variable models, trained using variational autoencoders or generative adversarial networks, are now a key technique for representation learning of continuous structures. However, applying similar methods to discrete structures, such as text sequences or discretized images, has proven to be more challenging.
Junbo Jake Zhao +4 more
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