Max pool layer vs Convolution with stride performance

Yes that can be done. Its explained in the paper ‘Striving for simplicity: The all convolutional net’ https://arxiv.org/pdf/1412.6806.pdf. Quote from the paper: ‘We find that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks’

What is the difference between Keras’ MaxPooling1D and GlobalMaxPooling1D functions?

Td;lr GlobalMaxPooling1D for temporal data takes the max vector over the steps dimension. So a tensor with shape [10, 4, 10] becomes a tensor with shape [10, 10] after global pooling. MaxPooling1D takes the max over the steps too but constrained to a pool_size for each stride. So a [10, 4, 10] tensor with pooling_size=2 … Read more

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