Recently, we've looked at convolutional layers and certain variations to see how they can be used in machine learning problems. Today, we'll focus on a variant called _transposed convolution_, which ...

Abstract: Transposed convolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for ...

of `kernels` must match with that of `inputs`. strides: A tuple of two positive `int`. padding: A non-negative `int`.

Abstract: This paper proposes a method for supervised classification using Low-Rank Representation of transposed data. Recent papers have suggested that low rank representation of transposed data may ...

it is convenient to not change the size of output or keep the dimensions of input and output the same. This can be achieved by the transposed convolution in a better way Convolutional Neural Network ...