Search Results for author:
Found 5 papers, 3 papers with code
Searching for MobileNetV3
We achieve new state of the art results for mobile classification, detection and segmentation.
Ranked #8 on
Dichotomous Image Segmentation
on DIS-TE1
Can weight sharing outperform random architecture search? An investigation with TuNAS
Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models.
Geo-Aware Networks for Fine-Grained Recognition
By leveraging geolocation information we improve top-1 accuracy in iNaturalist from 70. 1% to 79. 0% for a strong baseline image-only model.
Discovering Multi-Hardware Mobile Models via Architecture Search
Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored.
Multi-path Neural Networks for On-device Multi-domain Visual Classification
This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.
