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图像分类经典论文翻译汇总:[翻译汇总]
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Deep Residual Learning for Image Recognition
Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun
Microsoft Research
{kahe, v-xiangz, v-shren, jiansun}@microsoft.com
深度残差学习的图像识别
何恺明 张翔宇 任少卿 孙剑
微软研究院
{kahe, v-xiangz, v-shren, jiansun}@microsoft.com
Abstract
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 used previously. We explicitly reformulate
the layers as learning residual functions with reference to the layer inputs,
instead of learning unreferenced functions. We provide comprehensive
empirical evidence showing that these residual networks are easier to
optimize, and can gain accuracy from considerably increased depth. On the
ImageNet dataset we evaluate residual nets with a depth of up to 152 layers
— 8× deeper than VGG nets [40] but still having lower complexity. An
ensemble of these residual nets achieves 3.57% error on the ImageNet test
set. This result won the 1st place on the ILSVRC 2015 classification task.
We also present analysis on CIFAR-10 with 100 and 1000 layers.
摘要