As we may have inferred directly from its formula, the convolution operation leads either to a decrease or no change in the size of the output. This’s helpful in classification problems, but there are some instances where we need to go the other way.
Note: The animations to explain the convolution mechanics are used here with special thanks from Vincent Dumoulin, Francesco Visin - A guide to convolution arithmetic for deep learning [arXiv:1603.07285]
In transposed convolution, we go the other way around, where the input image is upsampled and usually increases in size. This is often helpful in image generation using a GAN/VAE or superresolution, and so on.