Convolution transposée
A transposed convolution, also known as a fractionally strided convolution or deconvolution, is a technique used primarily in the field of apprentissage profond and vision par ordinateur. It is commonly employed in applications such as traitement d'image, modèles génératifs, and neural networks, particularly in architectures like autoencoders and generative adversarial networks (GANs).
In a standard convolution, an input tensor (like an image) is processed by a filter (or kernel) to produce a smaller output tensor. This operation helps extract features from the input data. Conversely, a transposed convolution aims to reverse this process, taking a smaller input tensor and expanding it into a larger output tensor. This is particularly useful for tasks like génération d'image and segmentation, where you want to create higher-resolution outputs from lower-resolution inputs.
The transposed convolution achieves this by applying a filter to the input tensor in a way that effectively spreads the information across a larger area. It does this by inserting zeros between the elements of the input tensor (a process known as “zero padding”) and then applying the opération de convolution. The result is that the output tensor has a greater spatial dimension than the input tensor.
Mathematically, if a standard convolution operation reduces the size of the input tensor, a transposed convolution does the opposite by utilizing the same kernel in a manner that expands the dimensions. This operation is defined by the stride and padding parameters, which control how the filter interacts with the input tensor.
En résumé, les convolutions transposées sont essentielles tools in deep learning for tasks that require upsampling, enabling models to generate high-resolution data from lower-dimensional representations.