This article proposes a data-free quantization technique called DFFG, based on fast gradient iteration, which uses information learned from the full-precision ...

Model quantization is a technique that optimizes neural network computation by converting weight parameters and activation values from floating-point ...

This article proposes a data-free quantization technique called DFFG, based on fast gradient iteration. DFFG uses information learned from the full-precision ...

DFFG: Fast Gradient Iteration for Data-free Quantization. H Leng, S Fang, Y ... A Target Tracking Method Based on Particle Filter and Multi-feature Fusion.

Nov 4, 2021 · This article proposes a data-free quantization technique called DFFG, based on fast gradient iteration, which uses information learned from ...

Jun 25, 2024 · DFFG: Fast Gradient Iteration for Data-free Quantization. BMVC. 2023 | Conference paper. Contributors: Leng, Huixing; Fang, Shuangkang; Wang ...

DFFG: Fast Gradient Iteration for Data-free Quantization. H Leng, S Fang, Y Wang, Z Zhang, D Qi, W Ding. BMVC, 514-520, 2023. 2023. 系统目前无法执行此操作，请 ...

DFFG: Fast Gradient Iteration for Data-free Quantization Huixing Leng, Shuangkang Fang* (Equal Contribution), Yufeng Wang, Zehao Zhang, Dacheng Qi, Wenrui ...

This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with ...

Missing: DFFG: Fast Gradient