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This layer expects the channels to be last, i.e. the input shape should be
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image height,``W`` is the input image width, and``C`` is the number of
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This layer expects the channels to be last, i.e. the input shape should be
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image height,``W`` is the input image width, and``C`` is the number of
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maintain the expected value of each element. Unlike traditional dropout,
which zeros individual entries, this layer zeros entire channels. This is
beneficial for early convolution layers where adjacent pixels are
correlated. In such case, traditional dropout may not effectively
regularize activations. For more details, see [1].<2E>h]<5D>(h<16>%The remaining channels are scaled by <20><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>hhhNhNubj<62>)<29><>}<7D>(h<05>:math:`\frac{1}{1-p}`<60>h]<5D>h<16> \frac{1}{1-p}<7D><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+j<>hj<>ubhXM to
maintain the expected value of each element. Unlike traditional dropout,
which zeros individual entries, this layer zeros entire channels. This is
beneficial for early convolution layers where adjacent pixels are
correlated. In such case, traditional dropout may not effectively
regularize activations. For more details, see [1].<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>hhhNhNubeh}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+jjhjzhK hjghhubjk)<29><>}<7D>(h<05><>[1]: Thompson, J., Goroshin, R., Jain, A., LeCun, Y. and Bregler C., 2015.
Efficient Object Localization Using Convolutional Networks. CVPR 2015.<2E>h]<5D>h<16><>[1]: Thompson, J., Goroshin, R., Jain, A., LeCun, Y. and Bregler C., 2015.
Efficient Object Localization Using Convolutional Networks. CVPR 2015.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hj<>hhhNhNubah}<7D>(h!]<5D>h#]<5D>h%]<5D>h']<5D>h)]<5D>uh+jjhjzhKhjghhubh <09>
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