ResNet-D
2019/11/12
前言:
論文《Deep networks with stochastic depth》,我將其命名為 ResNet-D,取其名稱中的 depth。此外,論文裡的實驗隨機丟棄一些殘差層來訓練網路,使用的技巧是 dropout。經由這樣的設計,也可將原始的殘差網路從一百層提升到一千層。
-----
// Review Stochastic Depth (Image Classification) - Towards Data Science
-----
// Review Stochastic Depth (Image Classification) - Towards Data Science
-----
// Review Stochastic Depth (Image Classification) - Towards Data Science
-----
// Review Stochastic Depth (Image Classification) - Towards Data Science
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
# ResNet-D
-----
References
◎ 論文
# ResNet-D
Huang, Gao, et al. "Deep networks with stochastic depth." European conference on computer vision. Springer, Cham, 2016.
https://arxiv.org/pdf/1603.09382.pdf -----
◎ 英文參考資料
Review Stochastic Depth (Image Classification) - Towards Data Science
https://towardsdatascience.com/review-stochastic-depth-image-classification-a4e225807f4a
-----
◎ 簡體中文參考資料
Deep Networks with Stochastic Depth
https://bingning.wang/research/Article/?id=59
No comments:
Post a Comment