V
Vineeth Bhaskara
Researcher at Samsung
Publications - 4
Citations - 106
Vineeth Bhaskara is an academic researcher from Samsung. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 2, co-authored 4 publications receiving 53 citations.
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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang,Martin Danelljan,Yawei Li,Radu Timofte,Jie Liu,Jie Tang,Gangshan Wu,Yu Zhu,Xiangyu He,Wenjie Xu,Chenghua Li,Cong Leng,Jian Cheng,Guangyang Wu,Wenyi Wang,Xiaohong Liu,Hengyuan Zhao,Xiangtao Kong,Jingwen He,Yu Qiao,Chao Dong,Xiaotong Luo,Liang Chen,Jiangtao Zhang,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Xiaochuan Li,Zhiqiang Lang,Jiangtao Nie,Wei Wei,Lei Zhang,Abdul Muqeet,Jiwon Hwang,Subin Yang,JungHeum Kang,Sung-Ho Bae,Yongwoo Kim,Yanyun Qu,Geun-Woo Jeon,Jun-Ho Choi,Jun-Hyuk Kim,Jong-Seok Lee,Steven Marty,Eric Marty,Dongliang Xiong,Siang Chen,Lin Zha,Jiande Jiang,Xinbo Gao,Wen Lu,Haicheng Wang,Vineeth Bhaskara,Alex Levinshtein,Stavros Tsogkas,Allan D. Jepson,Xiangzhen Kong,Tongtong Zhao,Shanshan Zhao,Hrishikesh P S,Densen Puthussery,C. V. Jiji,Nan Nan,Shuai Liu,Jie Cai,Zibo Meng,Jiaming Ding,Chiu Man Ho,Xuehui Wang,Qiong Yan,Yuzhi Zhao,Long Chen,Long Sun,Wenhao Wang,Zhenbing Liu,Rushi Lan,Rao Muhammad Umer,Christian Micheloni +77 more
TL;DR: The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
Book ChapterDOI
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang,Martin Danelljan,Yawei Li,Radu Timofte,Jie Liu,Jie Tang,Gangshan Wu,Yu Zhu,Xiangyu He,Wenjie Xu,Chenghua Li,Cong Leng,Jian Cheng,Guangyang Wu,Wenyi Wang,Xiaohong Liu,Hengyuan Zhao,Xiangtao Kong,Jingwen He,Yu Qiao,Chao Dong,Xiaotong Luo,Liang Chen,Jiangtao Zhang,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Xiaochuan Li,Zhiqiang Lang,Jiangtao Nie,Wei Wei,Lei Zhang,Abdul Muqeet,Jiwon Hwang,Subin Yang,JungHeum Kang,Sung-Ho Bae,Yongwoo Kim,Yanyun Qu,Geun-Woo Jeon,Jun-Ho Choi,Jun-Hyuk Kim,Jong-Seok Lee,Steven Marty,Eric Marty,Dongliang Xiong,Siang Chen,Lin Zha,Jiande Jiang,Xinbo Gao,Wen Lu,Haicheng Wang,Vineeth Bhaskara,Alex Levinshtein,Stavros Tsogkas,Allan D. Jepson,Xiangzhen Kong,Tongtong Zhao,Shanshan Zhao,P. S. Hrishikesh,Densen Puthussery,C. V. Jiji,Nan Nan,Shuai Liu,Jie Cai,Zibo Meng,Jiaming Ding,Chiu Man Ho,Xuehui Wang,Qiong Yan,Yuzhi Zhao,Long Chen,Long Sun,Wenhao Wang,Zhenbing Liu,Rushi Lan,Rao Muhammad Umer,Christian Micheloni +77 more
TL;DR: The AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results as discussed by the authors was held in 2019, where the goal was to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption.
Book ChapterDOI
Efficient Super-Resolution Using MobileNetV3
TL;DR: In this article, the authors adapted MobileNetV3 blocks, shown to work well for classification, detection and segmentation, to the task of super-resolution, which can potentially improve the digital zoom capabilities of most modern mobile phones, but are not directly applicable on device, due to hardware constraints.
Posted Content
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks.
TL;DR: Gradient Normalization (GraN) as mentioned in this paper introduces a piecewise K-Lipschitz constraint in the input space, where each piece handles a subset of input space and the gradients per subset are piecewise constant.