420 citations · 902 across the 15 of their papers we have counts for
4 papers · 1 filter
Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters
Kaifeng Bi, Changping Hu, Lingxi Xie +3
DARTS is a popular algorithm for neural architecture search (NAS). Despite its great advantage in search efficiency, DARTS often suffers weak stability, which reflects in the large…
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
Zhuoxun He, Lingxi Xie, Xin Chen +3
Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks. Recently, researchers proposed a f…
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
Yuhui Xu, Lingxi Xie, Xiaopeng Zhang +4
Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly tr…
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
Xin Chen, Lingxi Xie, Jun Wu +1
Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search. However, these approaches often report lower acc…