【論文閱讀筆記】PACT：PArameterized Clipping Activation for Quantized Neural Networks
全文概括 本文目的是將 activation 和 weight 一起量化，其結果能量化 activation 和 weight 到 4-bit 大小，且準確度能和 full precision 媲美(在一系列流行的模型和數據集上)。 該方法是提出一個新的激活函數，即PACT(PArameterized Clipping Activation)，其作用在訓練階段，即該方案是從頭開始訓練的。
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相關文章
- 1. PACT: PARAMETERIZED CLIPPING ACTIVATION FOR QUANTIZED NEURAL NETWORKS
- 2. 【論文閱讀筆記】-《Quantized Convolutional Neural Networks for Mobile Devices》
- 3. 【Quantized Convolutional Neural Networks for Mobile Devices】論文筆記
- 4. 論文筆記：Exploring Neural Networks with Activation Atlases
- 5. Global Sparse Momentum SGD for Pruning Very Deep Neural Networks 論文閱讀筆記
- 6. 【論文閱讀筆記】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 7. 【論文閱讀筆記】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks
- 8. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 9. 論文閱讀：Deep Neural Networks for Object Detection
- 10. 論文閱讀-Fast Algorithms for Convolutional Neural Networks