What does 1x1 convolution mean in a neural network? $\begingroup$ 1x1 conv creates channel-wise dependencies with a negligible cost This is especially exploited in depthwise-separable convolutions This is especially exploited in depthwise-separable convolutions
What is the difference between Conv1D and Conv2D? I will be using a Pytorch perspective, however, the logic remains the same When using Conv1d(), we have to keep in mind that we are most likely going to work with 2-dimensional inputs such as one-hot-encode DNA sequences or black and white pictures
Convolutional Layers: To pad or not to pad? - Cross Validated Quote from Stanford lectures: "In addition to the aforementioned benefit of keeping the spatial sizes constant after CONV, doing this actually improves performance 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
In CNN, are upsampling and transpose convolution the same? Both the terms "upsampling" and "transpose convolution" are used when you are doing "deconvolution" ( lt;-- not a good term, but let me use it here) Originally, I thought that they mean the same t
Where should I place dropout layers in a neural network? $\begingroup$ 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 $\endgroup$ –
machine learning - RNN vs Convolution 1D - Cross Validated Intuitively, are both RNN and 1D conv nets more or less the same? I mean the input shape for both are 3-D tensors, with the shape of RNN being ( batch, timesteps, features) and the shape of 1D conv nets being (batch, steps, channels) They are both used for tasks involving sequences like time series, NLP etc So my question here is this,
machine learning - How to convert fully connected layer into . . . In this example, as far as I understood, the converted CONV layer should have the shape (7,7,512), meaning (width, height, feature dimension) And we have 4096 filters And the output of each filter's spatial size can be calculated as (7-7+0) 1 + 1 = 1 Therefore we have a 1x1x4096 vector as output