86 citations · 206 across the 5 of their papers we have counts for
5 papers
MixPath: A Unified Approach for One-shot Neural Architecture Search
Xiangxiang Chu, Shun Lu, Xudong Li +1
Blending multiple convolutional kernels is proved advantageous in neural architecture design. However, current two-stage neural architecture search methods are mainly limited to si…
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…
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…
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…