This is with refernce to the paper Efficient Object Localization Using Convolutional Networks, and from what I understand the dropout is implemented in 2D.

After reading the code from Keras on how the Spatial 2D Dropout is implemented, basically a random binary mask of shape [batch_size, 1, 1, num_channels] is implemented. However, what does this spatial 2D Dropout exactly do to the input convolution block of shape [batch_size, height, width, num_channels]?

My current guess is that for each pixel, if any of the pixel's layers/channels has a negative value, the entire channels of that one pixel will be defaulted to zero. Is this correct?

However, if my guess is correct, then how does using a binary mask of shape [batch_size, height, width, num_channels] that are exactly in the dimension of the original input block give the usual element-wise dropout (this is according to the tensorflow's original dropout implementation that sets the shape of the binary mask as the shape of the input)? Because it would then mean if any pixel in the conv block is negative, then the entire conv block will be defaulted to 0. This is the confusing part I don't quite understand.

Thank you for your help.

This is with refernce to the paper Efficient Object Localization Using Convolutional Networks, and from what I understand the dropout is implemented in 2D.

After reading the code from Keras on how the Spatial 2D Dropout is implemented, basically a random binary mask of shape [batch_size, 1, 1, num_channels] is implemented. However, what does this spatial 2D Dropout exactly do to the input convolution block of shape [batch_size, height, width, num_channels]?

My current guess is that for each pixel, if any of the pixel's layers/channels has a negative value, the entire channels of that one pixel will be defaulted to zero. Is this correct?

However, if my guess is correct, then how does using a binary mask of shape [batch_size, height, width, num_channels] that are exactly in the dimension of the original input block give the usual element-wise dropout (this is according to the tensorflow's original dropout implementation that sets the shape of the binary mask as the shape of the input)? Because it would then mean if any pixel in the conv block is negative, then the entire conv block will be defaulted to 0. This is the confusing part I don't quite understand.