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Liqun Cheng

2 accepted papers

2023

TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching

ICCV 2023poster

One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence. Previous works designed expansion operators to scale up pretrained models to the target model before tr…

Cited by 2PDFScholar
2021

Searching for Fast Model Families on Datacenter Accelerators

CVPR 2021poster

Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the e…

Cited by 43PDFcodeScholar