LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
ECCV-2018 ABSTRACT 雖然權重和激活值的量化是深度神經網絡（DNN）壓縮的有效方法，而且具備很大的潛力來提升利用位操做的推理速度，可是在量化模型和full precision模型之間的預測精度方面仍然存在明顯的差距。爲了彌補這個差距，咱們提出了對量化的，位操做兼容的DNN及其相關量化器的聯合訓練方法，而不是使用固定的手工量化方案，如均勻或對數量化。咱們學習量化器的方法適用於任意位精
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相關文章
- 1. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
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