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guanfuchen
related paper |摘要| |---| |In this paper, we propose a generative model, Temporal Generative Adversarial Nets (TGAN), which can learn a semantic representation of unlabeled videos, and is capable of...
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related paper |摘要| |---| |In this work we investigate **the effect of the convolutional network depth** on its accuracy in the large-scale image recognition setting. Our main contribution is a...
related paper |摘要| |---| |Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent...
删除线表示任务完成或基本完成。 - 阅读论文Residual Networks Behave Like Ensembles of Relatively Shallow Networks，增加对残差网络的深入理解。 - ~~阅读论文Wide residual networks，对wide残差网络有更深入的理解~~，实现相关代码，参考 #2 - 阅读论文Systematic evaluation of CNN advances on the ImageNet，对ImageNet上取得的相关进展进行了解。
related paper |摘要| |---| |We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new...
related paper |摘要| |---| |The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional...
related paper |摘要| |---| |Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those...