Search Results for author:
Found 5 papers, 5 papers with code
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
To discover powerful yet compact models is an important goal of neural architecture search.
Ranked #76 on
Neural Architecture Search
on ImageNet
MoGA: Searching Beyond MobileNetV3
Bearing the target hardware in mind, we propose the first Mobile GPU-Aware (MoGA) neural architecture search in order to be precisely tailored for real-world applications.
Ranked #854 on
Image Classification
on ImageNet
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
We demonstrate that this is crucial for improving the confidence of models' ranking.
Ranked #3 on
Neural Architecture Search
on CIFAR-10
(using extra training data)
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
Deep convolutional neural networks demonstrate impressive results in the super-resolution domain.
Ranked #16 on
Image Super-Resolution
on BSD100 - 2x upscaling
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
In this paper, we present a new multi-objective oriented algorithm called MoreMNAS (Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search) by leveraging good virtues from both EA and RL.
