What is a transposed convolution?

A transposed convolution is a learnable operation that can increase the height and width of a CNN feature map. It is commonly used in decoder networks to turn small, coarse feature maps into larger ones.

How does it work?

• A standard convolution with stride greater than 1 often reduces spatial resolution.
• A transposed convolution with stride greater than 1 can increase spatial resolution while learning how to combine features.
• Conceptually, it can be understood as spacing input values apart and applying learned convolutional filters; overlapping contributions are added together.

Although it is sometimes called *deconvolution*, it is not the mathematical inverse of convolution and cannot automatically recover information lost during downsampling.

Practical example

Suppose a CNN decoder receives 16 feature channels of size `4×4`. We want to produce 8 channels of size `8×8`.

Use this PyTorch layer:

nn.ConvTranspose2d(16, 8, kernel_size=4, stride=2, padding=1)

• Input shape: (1, 16, 4, 4) (batch, channels, height, width).
• Output shape: (1, 8, 8, 8).
• For dilation 1 and output padding 0, the spatial size is (4 - 1) × 2 - 2 × 1 + 4 = 8.

Thus, the layer doubles height and width while learning how to construct the larger feature maps.

Where is it useful?

• Image segmentation: Decoder layers recover a higher-resolution prediction from compact image features.
• Autoencoders: A decoder expands compressed feature maps.
• Image generators: Learned upsampling turns lower-resolution representations into images.

Important limitation: Certain kernel/stride combinations can create uneven overlap and checkerboard artifacts. A common alternative is interpolation followed by an ordinary convolution; the next questions compare these approaches.

Related: “What is a convolutional layer?” and “What is a feature map in a CNN?”

Research: A guide to convolution arithmetic for deep learning. Documentation: PyTorch ConvTranspose2d.