Convolutional adaptive denoising autoencoders for hierarchical feature extraction

By Qianjun Zhang, Lei Zhang. 2018

25 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Generative Models, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yang Song
2. Stefano Ermon
3. Yubei Chen
4. Arash Vahdat
5. Friedrich T. Sommer
6. Zengyi Li
7. Luc Van Gool
8. Karsten Kreis

In Generative Models

1. Yang Song
2. Stefano Ermon
3. Arash Vahdat
4. Yubei Chen
5. Zengyi Li
6. Friedrich T. Sommer
7. Karsten Kreis
8. Jonathan Ho

In Signal Processing

1. Yang Song
2. Yubei Chen
3. Stefano Ermon
4. Zengyi Li
5. Friedrich T. Sommer
6. Arash Vahdat
7. Jong Chul Ye

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.”
• 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.”