Also known as transposed convolutional or deconvolutional layers, disconvolutional layers are commonly used in neural networks for image processing.
Essentially, these layers are the inverse of a traditional convolutional layer. They take input from a previous layer and process it in such a way as to produce output that the next layer in the network can use. The most common use of disconvolutional layers is for image upsampling. By using transposed filters or kernels, disconvolutional layers generate outputs with larger dimensions than what was originally given as an input.
This allows images to be reconstructed with more detail compared to other interpolation methods like bilinear or bicubic interpolations. Disconvolutional layers also perform better than traditional pooling techniques (such as max pooling) due to their ability to retain spatial information during upsampling operations. As a result, they often improve accuracy when used in classification tasks involving images.
The input is convolved with filters in a regular convolutional layer to produce feature maps. Disconvolutional layers reverse the process: feature maps are convolved with filters to produce higher-resolution outputs. 685
This process can be used to generate pixel-level predictions from feature maps produced by lower layers of a neural network.
Although "disconvolution" implies that it is the inverse of convolution, this is not technically accurate. Disconvolutional layers are not true inverse operations of convolutional layers, as they do not guarantee to the production of the exact input that was convolved to create the feature maps in the first place.
Convolutional layers work by applying a set of learnable filters to input feature maps. These filters are usually larger than those in the convolutional layer to expand the feature maps to a higher resolution. As a result, the output has a higher resolution than the input when the filters are applied with a stride greater than 1.
For example, convolutional layers can generate high-resolution images from low-resolution inputs, upsampling feature maps to perform semantic segmentation or generate object masks for detecting objects. When a network's input and output dimensions do not match, such as when upsampling an image, they are especially useful.
In addition to being computationally expensive, disconvolutional layers are prone to producing artifacts like checkerboard patterns in the output. Several techniques have addressed these issues, such as stridden convolutions instead of transposed convolutions or bilinear upsampling followed by convolutional layers.
As a result, disconvolutional layers are powerful deep-learning tools that can be applied to image processing. In semantic segmentation and object detection tasks, they can generate higher-resolution outputs from lower-resolution inputs. It's important to take care when using them, as they can be computationally expensive and result in artifacts. 685