Adaptive Noise Schedule for Denoising Autoencoder

By B. Chandra, Rajesh Kumar Sharma. 2014

33 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models

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Citations in the corpus

Generative Modeling by Estimating Gradients of the Data Distribution (2019, background): “In [14, 7, 66], different annealing schedules on the noise for training denoising autoencoders are proposed.”
Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional Spaces (2019, methodology): “Our approach is conceptually similar to the idea of combining denoising autoencoder and annealing (Geras and Sutton, 2015; Chandra and Sharma, 2014; Zhang and Zhang, 2018) though they were proposed in the context of pre-training neural networks for classification applications.”
Research on denoising sparse autoencoder (2016, methodology): “Autoencoder has not only got the attention of theorists, but also been widely applied to practice for solving machine learning tasks [8–12].”
Scheduled denoising autoencoders (2014, methodology): “We use a procedure similar to one used, for example, by Coates et al. (2011)1.”
Convolutional adaptive denoising autoencoders for hierarchical feature extraction (2018, methodology): “Regarding the selection of noise level, researchers have proposed numerous new methods [19, 20].”
Learning Energy-Based Models in High-Dimensional Spaces with Multiscale Denoising-Score Matching (2019, methodology): “Our approach is conceptually similar to the idea of combining denoising autoencoder and annealing (Geras and Sutton, 2015; Chandra and Sharma, 2014; Zhang and Zhang, 2018) though this idea was proposed in the context of pre-training neural networks for classification applications.”