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There is large consent that successful training of deep net-works requires many thousand annotated training samples. In this pa-per, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localiza-tion. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neu-ronal structures in electron microscopic stacks. Using the same net-work trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these cate-gories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Ca e) and the trained networks are available at http:
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commented
2 years ago
设备信息
描述问题
沙拉查词的谷歌翻译结果和在谷歌翻译官方页面结果不一致，并且出现较大错误
复现步骤
期待的正常行为
应和谷歌翻译官网一致
截图
这是该段落的官方翻译：
这是沙拉查词的：
额外信息
沙拉查词很好用，但这个问题确实有点影响体验，毕竟谷歌翻译一般比百度翻译要准确， 我之前还以为百度翻译更厉害😂，后来发现是这个bug的问题
附上设置：