@@ -26,6 +26,7 @@ Pruning algorithms compress the original network by removing redundant weights o
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@@ -26,6 +26,7 @@ Pruning algorithms compress the original network by removing redundant weights o
| [ActivationAPoZRankFilterPruner](./Pruner.md#ActivationAPoZRankFilterPruner) | Pruning filters based on the metric APoZ (average percentage of zeros) which measures the percentage of zeros in activations of (convolutional) layers. [Reference Paper](https://arxiv.org/abs/1607.03250) |
| [ActivationAPoZRankFilterPruner](./Pruner.md#ActivationAPoZRankFilterPruner) | Pruning filters based on the metric APoZ (average percentage of zeros) which measures the percentage of zeros in activations of (convolutional) layers. [Reference Paper](https://arxiv.org/abs/1607.03250) |
| [ActivationMeanRankFilterPruner](./Pruner.md#ActivationMeanRankFilterPruner) | Pruning filters based on the metric that calculates the smallest mean value of output activations |
| [ActivationMeanRankFilterPruner](./Pruner.md#ActivationMeanRankFilterPruner) | Pruning filters based on the metric that calculates the smallest mean value of output activations |
| [Slim Pruner](./Pruner.md#slim-pruner) | Pruning channels in convolution layers by pruning scaling factors in BN layers(Learning Efficient Convolutional Networks through Network Slimming) [Reference Paper](https://arxiv.org/abs/1708.06519) |
| [Slim Pruner](./Pruner.md#slim-pruner) | Pruning channels in convolution layers by pruning scaling factors in BN layers(Learning Efficient Convolutional Networks through Network Slimming) [Reference Paper](https://arxiv.org/abs/1708.06519) |
| [TaylorFO Pruner](./Pruner.md#taylorfoweightfilterpruner) | Pruning filters based on the first order taylor expansion on weights(Importance Estimation for Neural Network Pruning) [Reference Paper](http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf) |
