Training with Quantization Noise for Extreme Model Compression
https://arxiv.org/pdf/2004.07320.pdf 論文的核心思想以下：web 針對上圖進行實例分析： 如對卷積層weight爲（64,64,3，3）進行量化網絡 設置centroids數目如（32,9）其中9表示3*3卷積核的flatten 經過k-means進行選取centroids 以後進行編碼（codebook），生成三個矩陣：svg centroids=（32,9）
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相關文章
- 1. Training with Quantization Noise for Extreme Model Compression
- 2. 神經網絡參數量化，模型壓縮論文筆記Training with Quantization Noise for Extreme Model Compression
- 3. Model Compression and Acceleration Overview
- 4. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co
- 5. 2018-22Deep Compression: Compression Deep Neural Networks With Pruning, Trained Quantization And...
- 6. 論文閱讀《A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising》CVPR2020
- 7. 【論文閱讀筆記】Deep Neural Network Compression with Single and Multiple Level Quantization
- 8. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 9. Compression Deep Neural Networks With Pruning, Trained Quantization And Huffman Coding
- 10. 【2】Model Compression As Constrained Optimization, with Application to Neural Nets. 2017