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A06B-6110-H015
第一个深度神经网络,其主要特点包括:
1. 使用ReLU作为激活函数。
2. 提出在全连接层使用Dropout避免过拟合。注:当BN提出后,Dropout就被BN替代了。
3. 由于GPU显存太小,使用了两个GPU,做法是在通道上分组。
4. 使用局部响应归一化(Local Response Normalization --LRN),在生物中存在侧抑制现象,即被激活的神经元会抑制周围的神经元。在这里的目的是让局部响应值大的变得相对更大,并抑制其它响应值相对比较小的卷积核。例如,某特征在这一个卷积核中响应值比较大,则在其它相邻卷积核中响应值会被抑制,这样一来卷积核之间的相关性会变小。LRN结合ReLU,使得模型提高了一点多个百分点。
5. 使用重叠池化。作者认为使用重叠池化会提升特征的丰富性,且相对来说会更难过拟合。
A06B-6110-H015
A06B-6110-H015
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