35 citations · 83 across the 10 of their papers we have counts for
12 papers
DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling
Bicheng Guo, Shuxuan Guo, Miaojing Shi +4
Differentiable architecture search (DARTS) has been a mainstream direction in automatic machine learning. Since the discovery that original DARTS will inevitably converge to poor a…
Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers
Tao Tang, Changlin Li, Guangrun Wang +3
Automatic data augmentation (AutoAugment) strategies are indispensable in supervised data-efficient training protocols of vision transformers, and have led to state-of-the-art resu…
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
Kaicheng Yu, Tang Tao, Hongwei Xie +10
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR. The camera provides rich semantic information such as color, texture, and the LiDA…
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy
Yash Mehta, Colin White, Arber Zela +6
The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural archit…
An Analysis of Super-Net Heuristics in Weight-Sharing NAS
Kaicheng Yu, René Ranftl, Mathieu Salzmann
Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware. Existing methods in this space rely on a diverse set of heuristics to design…
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
Kaicheng Yu, Rene Ranftl, Mathieu Salzmann
Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically s…