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讲座时间：2024年 3 月 22 日 14时00 分

讲座地点：工B105

讲座对象：感兴趣的师生

讲座摘要：

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 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—8x deeper than VGG nets but still having lower complexity.