3 papers
cs.CV2024
PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer
Pierre-David Letourneau, Manish Kumar Singh, Hsin-Pai Cheng +6
We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Not…
cs.CV2023
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks
Sweta Priyadarshi, Tianyu Jiang, Hsin-Pai Cheng +3
With the growing demand for vision applications and deployment across edge devices, the development of hardware-friendly architectures that maintain performance during device deplo…
cs.CV2023
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks
Kartikeya Bhardwaj, Hsin-Pai Cheng, Sweta Priyadarshi +1
Zero-Shot Neural Architecture Search (NAS) approaches propose novel training-free metrics called zero-shot proxies to substantially reduce the search time compared to the tradition…