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... | @@ -51,6 +51,8 @@ L1 norm 크기를 기반으로 필터 마스크를 생성하여 가중치 학습 | ... | @@ -51,6 +51,8 @@ L1 norm 크기를 기반으로 필터 마스크를 생성하여 가중치 학습 |
51 | - ![image](https://user-images.githubusercontent.com/41580746/102396706-28a58780-4020-11eb-9ebb-6cc723b4fcbf.png) | 51 | - ![image](https://user-images.githubusercontent.com/41580746/102396706-28a58780-4020-11eb-9ebb-6cc723b4fcbf.png) |
52 | - 기존 동적 필터 가지치기 대비 Accuracy 1.47%, Sparsity 1.08% 증가 | 52 | - 기존 동적 필터 가지치기 대비 Accuracy 1.47%, Sparsity 1.08% 증가 |
53 | 53 | ||
54 | +--- | ||
55 | + | ||
54 | _References_ | 56 | _References_ |
55 | - [1] Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Deep learning. Nature 521, 436-444, 2015. | 57 | - [1] Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Deep learning. Nature 521, 436-444, 2015. |
56 | - [2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jain Sun. Deep Residual Learning for Image Recognition. 2015. | 58 | - [2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jain Sun. Deep Residual Learning for Image Recognition. 2015. | ... | ... |
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