Asked about Convolution arithmetic and a CNN in PyTorch
What is the output shape of a 3x3 convolution with stride 2 and padding 1 on a 64x64x32 input with 64 filters, and how many parameters does it have?
An answer that works
Spatially floor((64 - 3 + 2) / 2) + 1 = 32, so 32x32x64. Parameters: 3 x 3 x 32 x 64 = 18,432 weights plus 64 biases, 18,496.
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Why do residual connections help very deep networks train?
Each block learns a correction added to its input, so an unhelpful block can stay close to the identity, and gradients flow directly through the additions to early layers without shrinking at every layer.
What gets marked up
Marked up for getting the arithmetic right out loud. Interviews for vision roles often include exactly this.
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