INQ(incremental network quantization：towards lossless CNNs with low-precision weights
incremental network quantization：towards lossless CNNs with low-precision weights 實驗前提：a.一個預訓練的模型，模型已收斂 b.訓練這個模型訓練集，驗證集，測試集 大體三個步驟：weights partition、group-wise quantization 、re-trainin
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相關文章
- 1. INCREMENTAL NETWORK QUANTIZATION: TOWARDS LOSSLESS CNNS WITH LOW-PRECISION WEIGHTS
- 2. Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights論文
- 3. INQ 論文解讀：Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
- 4. 【論文閱讀筆記】Incremental Network Quantizatio：Towards Lossless CNNs with Low-Precision Weights
- 5. 增量量化網絡：towards lossless CNNs with low-precision weights
- 6. 神經網絡壓縮（8）Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights
- 7. 二值網絡--TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights
- 8. 【論文閱讀筆記】Deep Neural Network Compression with Single and Multiple Level Quantization
- 9. Elman network with additional notes
- 10. BinaryConnect： Training Deep Neural Networks with binary weights during propagations