Multi-source adaptation joint kernel sparse representation for visual classification

By Jianwen Tao, Wenjun Hu, Shiting Wen. 2016

8 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Artificial Intelligence

1. Samuel F. Dodge
2. Umapada Pal
3. Saumik Bhattacharya
4. Subhankar Ghosh
5. Lina Karam
6. Prasun Roy

In Computer Vision and Pattern Recognition

1. Samuel F. Dodge
2. Lina Karam
3. Umapada Pal
4. Subhankar Ghosh
5. Saumik Bhattacharya
6. Prasun Roy
7. Matthias Bethge
8. Damon M. Chandler

Citations in the corpus

• Effects of Degradations on Deep Neural Network Architectures (2018, methodology): “[11] proposed joint kernel sparse representation based classification algorithm that performs satisfactorily on the database proposed in [10].”
• Understanding how image quality affects deep neural networks (2016, background): “[10] present an approach based on sparse representations that achieves good performance on this dataset.”
• Human and DNN Classification Performance on Images With Quality Distortions (2019, background): “Tao et al. (2016) develop an approach based on sparse representations that achieves good performance on this dataset.”