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Neural Architecture Search on Acoustic Scene Classification
Jixiang Li, Chuming Liang, Bo Zhang +3
Convolutional neural networks are widely adopted in Acoustic Scene Classification (ASC) tasks, but they generally carry a heavy computational burden. In this work, we propose a lig…
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search
Xiangxiang Chu, Tianbao Zhou, Bo Zhang +1
Differentiable Architecture Search (DARTS) is now a widely disseminated weight-sharing neural architecture search method. However, it suffers from well-known performance collapse d…
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Qingyuan Li +2
To discover powerful yet compact models is an important goal of neural architecture search. Previous two-stage one-shot approaches are limited by search space with a fixed depth. I…
MoGA: Searching Beyond MobileNetV3
Xiangxiang Chu, Bo Zhang, Ruijun Xu
The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its sup…
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Ruijun Xu
One of the most critical problems in weight-sharing neural architecture search is the evaluation of candidate models within a predefined search space. In practice, a one-shot super…
A Matrix-in-matrix Neural Network for Image Super Resolution
Hailong Ma, Xiangxiang Chu, Bo Zhang +1
In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper lay…
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Ruijun Xu +1
Fabricating neural models for a wide range of mobile devices demands for a specific design of networks due to highly constrained resources. Both evolution algorithms (EA) and reinf…
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Hailong Ma +2
Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by usi…