Unsupervised learning of compositional sparse code for natural image representation

By Yining Hong, Zhangzhang Si, Wenze Hu, Song-Chun Zhu, Ying Nian Wu. 2013

23 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Representation & Self-Supervised Learning

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. Song-Chun Zhu
2. Ying Nian Wu
3. Jianwen Xie
4. Yang Lu
5. Erik Nijkamp
6. Mitch Hill
7. Luc Van Gool
8. Wenze Hu

In Media Technology

1. Yang Lu
2. Song-Chun Zhu
3. Ying Nian Wu
4. Jianwen Xie

In Representation & Self-Supervised Learning

1. Song-Chun Zhu
2. Ying Nian Wu
3. Yang Lu
4. Jianwen Xie
5. Quanshi Zhang

Citations in the corpus

• A Theory of Generative ConvNet (2016, methodology): “A main motivation for this paper is to reconcile the FRAME model [43], where the Gabor wavelets play the role of bottom-up filters, and the Olshausen-Field model [25], where the wavelets play the role of top-down basis functions, and unfold these models into a hierarchical sparse compositional model”
• Learning FRAME Models Using CNN Filters (2015, methodology): “Such a scheme was used by (Hong et al. 2014) to learn codebooks of active basis models (Wu et al.”
• Learning Sparse FRAME Models for Natural Image Patterns (2014, methodology): “Here we follow the learning scheme in our previous work on compositional sparse coding (Hong et al. 2013).”