67 citations · 284 across the 17 of their papers we have counts for
7 papers · 1 filter
OMPQ: Orthogonal Mixed Precision Quantization
Yuexiao Ma, Taisong Jin, Xiawu Zheng +6
To bridge the ever increasing gap between deep neural networks' complexity and hardware capability, network quantization has attracted more and more research attention. The latest…
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient
Shaokun Zhang, Xiawu Zheng, Chenyi Yang +7
Despite superior performance on various natural language processing tasks, pre-trained models such as BERT are challenged by deploying on resource-constraint devices. Most existing…
ISTR: End-to-End Instance Segmentation with Transformers
Jie Hu, Liujuan Cao, Yao Lu +6
End-to-end paradigms significantly improve the accuracy of various deep-learning-based computer vision models. To this end, tasks like object detection have been upgraded by replac…
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
Yixu Wang, Jie Li, Hong Liu +4
Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we obse…
Lottery Jackpots Exist in Pre-trained Models
Yuxin Zhang, Mingbao Lin, Yunshan Zhong +2
Network pruning is an effective approach to reduce network complexity with acceptable performance compromise. Existing studies achieve the sparsity of neural networks via time-cons…
Distilling a Powerful Student Model via Online Knowledge Distillation
Shaojie Li, Mingbao Lin, Yan Wang +4
Existing online knowledge distillation approaches either adopt the student with the best performance or construct an ensemble model for better holistic performance. However, the fo…