What is the difference between Conv1D and Conv2D? I was going through the keras convolution docs and I have found two types of convultuion Conv1D and Conv2D I did some web search and this is what I understands about Conv1D and Conv2D; Conv1D is u
What does 1x1 convolution mean in a neural network? 1x1 conv creates channel-wise dependencies with a negligible cost This is especially exploited in depthwise-separable convolutions Nobody said anything about this but I'm writing this as a comment since I don't have enough reputation here
How do bottleneck architectures work in neural networks? We define a bottleneck architecture as the type found in the ResNet paper where [two 3x3 conv layers] are replaced by [one 1x1 conv, one 3x3 conv, and another 1x1 conv layer] I understand that t
Why does residual block in resnet shown as skipping not just 1-layer . . . Why is that when the diagram is talking about only skipping 1-layer, it's showing skip-connection after relu? Isn't that part of the second conv+relu layer? I've seen the input output feature map used Is the input feature map same as what is shown by 'x'? isn't weight layer mean the same thing as performing conv using a filter?
Where should I place dropout layers in a neural network? I've updated the answer to clarify that in the work by Park et al , the dropout was applied after the RELU on each CONV layer I do not believe they investigated the effect of adding dropout following max pooling layers
Convolutional Layers: To pad or not to pad? - Cross Validated If the CONV layers were to not zero-pad the inputs and only perform valid convolutions, then the size of the volumes would reduce by a small amount after each CONV, and the information at the borders would be “washed away” too quickly " -
In CNN, are upsampling and transpose convolution the same? Both the terms "upsampling" and "transpose convolution" are used when you are doing "deconvolution" (<-- not a good term, but let me use it here) Originally, I thought that they mean the same t