44 citations · 95 across the 9 of their papers we have counts for
5 papers · 1 filter
Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection
Yunqian Fan, Xiuying Wei, Ruihao Gong +4
Lane detection (LD) plays a crucial role in enhancing the L2+ capabilities of autonomous driving, capturing widespread attention. The Post-Processing Quantization (PTQ) could facil…
Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes
Ruihao Gong, Yang Yong, Zining Wang +4
Neural network sparsity has attracted many research interests due to its similarity to biological schemes and high energy efficiency. However, existing methods depend on long-time…
Lossy and Lossless (L) Post-training Model Size Compression
Yumeng Shi, Shihao Bai, Xiuying Wei +2
Deep neural networks have delivered remarkable performance and have been widely used in various visual tasks. However, their huge size causes significant inconvenience for transmis…
Equiangular Basis Vectors
Yang Shen, Xuhao Sun, Xiu-Shen Wei
We propose Equiangular Basis Vectors (EBVs) for classification tasks. In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle differen…
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Xiuying Wei, Ruihao Gong, Yuhang Li +2
Recently, post-training quantization (PTQ) has driven much attention to produce efficient neural networks without long-time retraining. Despite its low cost, current PTQ works tend…