训练集,在AI领域多指用于机器学习训练的数据,数据可以有标签的,也可以是无标签的。

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题目: ImageNet Classification with Deep Convolutional Neural Networks

摘要:

我们训练了一个大型的深度卷积神经网络,将LSVRC-2010 ImageNet训练集中的130万幅高分辨率图像分成1000个不同的类。在测试数据上,我们获得了前1名和前5名的错误率,分别为39.7%和18.9%,这比之前的最新结果要好得多。该神经网络有6000万个参数和50万个神经元,由5个卷积层组成,其中一些是最大池化层,还有两个全局连接层,最后是1000路的softmax。为了加快训练速度,我们使用了不饱和的神经元和一个非常高效的卷积网络GPU实现。为了减少全局连通层中的过拟合,我们采用了一种新的正则化方法,该方法被证明是非常有效的。

作者:

Ilya Sutskever是OpenAI的联合创始人和首席科学家,之前是斯坦福大学博士后,研究领域是机器学习,神经网络。

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In computer graphics, animation compression is essential for efficient storage, streaming and reproduction of animated meshes. Previous work has presented efficient techniques for compression by deriving skinning transformations and weights using clustering of vertices based on geometric features of vertices over time. In this work we present a novel approach that assigns vertices to bone-influenced clusters and derives weights using deep learning through a training set that consists of pairs of vertex trajectories (temporal vertex sequences) and the corresponding weights drawn from fully rigged animated characters. The approximation error of the resulting linear blend skinning scheme is significantly lower than the error of competent previous methods by producing at the same time a minimal number of bones. Furthermore, the optimal set of transformation and vertices is derived in fewer iterations due to the better initial positioning in the multidimensional variable space. Our method requires no parameters to be determined or tuned by the user during the entire process of compressing a mesh animation sequence.

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