Domain adaptive sparse representation-based classification
By He Zhang, Vishal M. Patel, Sumit Shekhar, Ramalingam Chellappa. 2015
32 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, 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 Biomedical Engineering
- Ramalingam Chellappa
- Vishal M. Patel
- Umberto Michieli
- Hossein Rahmani
- Jun Liu
- Mohammed Bennamoun
- Tae-Kyun Kim
- Wanqing Li
In Computer Vision and Pattern Recognition
- Pietro Zanuttigh
- Umberto Michieli
- Vishal M. Patel
- Ramalingam Chellappa
- Deepak Chandra
- Brandon Barbello
- Matthieu Cord
- Ming-Hsuan Yang
In Signal Processing
- Vishal M. Patel
- Ramalingam Chellappa
- Pietro Zanuttigh
- Umberto Michieli
- Deepak Chandra
- Brandon Barbello
- Carlos Domingo Castillo
- Weihong Deng
Citations in the corpus
- Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations (2021, background): “Only limited attention has been given on sparsity for deep learning architectures [2]; however, some prior techniques exist for domain adaptation on linear models exploiting sparse codes on a shared dictionary between the domains [54, 74].”
- Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges (2016, methodology): “One such method based on faces and touch gestures for continuous authentication using domain adaptation was recently proposed in [72].”