Transposed convolution layer

A transposed convolution layer, often used in deep learning, is a type of neural network layer that performs the opposite operation of a regular convolution layer. It is primarily used to increase the spatial dimensions (width and height) of the input data. This layer is commonly utilized in models that generate or reconstruct images, such as in autoencoders and generative adversarial networks (GANs).