【Paper Reading】Forward and Backward Information Retentionfor Accurate Binary Neural Networks
CVPR 2020 商湯科技 北航 文獻地址：https://arxiv.org/abs/1909.10788 源碼地址： https://github.com/htqin/IR-Net 摘要 權值和激活二值化是一種有效的深度神經網絡壓縮方法，可以利用位操作加速前向推理。雖然許多二值化方法通過最小化前向傳播的量化誤差來提高模型的精度，但二值化模型與全精度模型之間仍存在明顯的性能差距。我們的實證研究
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相關文章
- 1. Forward and Backward Information Retention for Accurate Binary Neural Networks
- 2. 【Paper Reading】Towards Accurate Binary Convolutional Neural
- 3. Paper Reading: Slimmable Neural Networks
- 4. Balanced Binary Neural Networks With Gated Residual
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- 10. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks