VEGA: Towards an End-to-End Configurable AutoML Pipeline

1 code implementation • 3 Nov 2020 • Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Xiaozhi Fang, Yixing Xu, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zhicheng Liu, Xinghao Chen, Kai Han, Han Shu, Dehua Song, Yunhe Wang, Wei zhang, Chunjing Xu, Zhenguo Li, Wenzhi Liu, Tong Zhang

Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.

BIG-bench Machine Learning Data Augmentation +3

Efficient Residual Dense Block Search for Image Super-Resolution

3 code implementations • 25 Sep 2019 • Dehua Song, Chang Xu, Xu Jia, Yiyi Chen, Chunjing Xu, Yunhe Wang

Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution.

Image Super-Resolution

Integral Neural Networks

1 code implementation • CVPR 2023 • Kirill Solodskikh, Azim Kurbanov, Ruslan Aydarkhanov, Irina Zhelavskaya, Yury Parfenov, Dehua Song, Stamatios Lefkimmiatis

We call such networks Integral Neural Networks (INNs).

AdderSR: Towards Energy Efficient Image Super-Resolution

no code implementations • CVPR 2021 • Dehua Song, Yunhe Wang, Hanting Chen, Chang Xu, Chunjing Xu, DaCheng Tao

To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks.

Image Classification Image Super-Resolution

Learning Frequency-aware Dynamic Network for Efficient Super-Resolution

no code implementations • ICCV 2021 • Wenbin Xie, Dehua Song, Chang Xu, Chunjing Xu, HUI ZHANG, Yunhe Wang

Extensive experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures to obtain the better tradeoff between visual quality and computational complexity.

Image Super-Resolution