Learning Accurate Performance Predictors for Ultrafast Automated Model Compression

In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing di...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	International journal of computer vision Ročník 131; číslo 7; s. 1761 - 1783
Hlavní autori:	Wang, Ziwei, Lu, Jiwen, Xiao, Han, Liu, Shengyu, Zhou, Jie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.07.2023 Springer Springer Nature B.V
Predmet:	Accuracy Artificial Intelligence Automation Complexity Computational efficiency Computer Imaging Computer Science Computing costs Image classification Image compression Image Processing and Computer Vision Object recognition Optimization Pattern Recognition Pattern Recognition and Graphics Performance prediction Searching Vision Compression policy optimization Performance predictor Evolutionary search Uncertainty estimation Automated model compression
ISSN:	0920-5691, 1573-1405
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!