Transposed Convolutions (Deep Learning)

Convolutional Neural Networks are used for computer vision projects and can be used to automatically extract features from inputs like photos and videos. These neural networks employ so-called convolutional layers that convolve (slide) over the input image, try to detect patterns, and adapt weights accordingly during the training process -- allowing learning to occur. Sometimes, however, we want the opposite to happen: invert the output of a convolutional layer and reconstruct the original input. This is for example the case with autoencoders, where we use normal convolutions to learn an encoded state and subsequently decode them into the original inputs. If done successfully, the encoded state can be used as a lower-dimensional representation of our input data, for dimensionality reduction.