【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
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
- 5. Towards Accurate Binary Convolutional Neural Network
- 6. Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Cla
- 7. #Paper Reading# Neural Extractive Summarization with Side Information
- 8. 【Paper Reading】【EAST: An Efficient and Accurate Scene Text Detector】
- 9. #Paper Reading# Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
- 10. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 更多相關文章...
- • W3C RDF and OWL 活動 - W3C 教程
- • XSL-FO table-and-caption 對象 - XSL-FO 教程
- • RxJava操作符（七）Conditional and Boolean
- • 使用阿里雲OSS+CDN部署前端頁面與加速靜態資源
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
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
- 5. Towards Accurate Binary Convolutional Neural Network
- 6. Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Cla
- 7. #Paper Reading# Neural Extractive Summarization with Side Information
- 8. 【Paper Reading】【EAST: An Efficient and Accurate Scene Text Detector】
- 9. #Paper Reading# Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
- 10. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks