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Doc for quantization #1881
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Doc for quantization #1881
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QuanluZhang
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Dec 27, 2019
QuanluZhang
reviewed
Dec 27, 2019
QuanluZhang
reviewed
Dec 27, 2019
QuanluZhang
reviewed
Dec 27, 2019
chicm-ms
reviewed
Dec 27, 2019
chicm-ms
reviewed
Dec 27, 2019
chicm-ms
reviewed
Dec 27, 2019
| @@ -30,6 +30,7 @@ We have provided several compression algorithms, including several pruning and q | |||
| | [Naive Quantizer](./Quantizer.md#naive-quantizer) | Quantize weights to default 8 bits | | |||
| | [QAT Quantizer](./Quantizer.md#qat-quantizer) | Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. [Reference Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)| | |||
| | [DoReFa Quantizer](./Quantizer.md#dorefa-quantizer) | DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [Reference Paper](https://arxiv.org/abs/1606.06160)| | |||
| | [BNN Quantizer](./Quantizer.md#BNN-Quantizer) | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. [Reference Paper](https://arxiv.org/abs/1602.02830)| | |||
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I think it's better to have a brief description of the concept Quantization before the table, same for Pruning
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I add a reference link to recent survey of model compression and brief introduction of Quantization and Pruning.
chicm-ms
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Dec 27, 2019
chicm-ms
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QuanluZhang
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Dec 29, 2019
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