Why can transposed convolutions create checkerboard artifacts?

Transposed convolutions can create checkerboard artifacts when their learned filters contribute unevenly to different output pixels. Some pixels receive more overlapping filter contributions than others, producing a repeating grid of brighter and darker patches.

Why does this happen?

• A transposed convolution spreads each input value over a region of the larger output.
• With some kernel-size and stride combinations, these regions overlap unevenly.
• The uneven contributions are multiplied across the two image dimensions, creating a checkerboard-like pattern.

This is especially common when the kernel size is not divisible by the stride.

Simple example

Take a 2×2 input containing only ones, and a single-channel transposed convolution with a 3×3 kernel of ones, stride 2, no padding, and no bias.

• The output is 5×5.
• Some outer output pixels receive one contribution.
• Pixels where the filter regions overlap receive two contributions in a row or column.
• The center pixel receives four contributions (2 × 2), making it brighter even though the input values are identical.

The resulting unequal brightness illustrates how repeated overlaps can produce visible grid patterns in generated or reconstructed images.

How can you reduce checkerboard artifacts?

• Resize then convolve: Upsample with nearest-neighbor or bilinear interpolation, then apply a regular convolution.
• Choose compatible kernel and stride: For example, a kernel size of 4 with stride 2 avoids the basic uneven-overlap pattern.
• Inspect generated outputs: Even compatible kernel/stride settings can still learn filters that introduce artifacts; they do not guarantee artifact-free results.

Where it matters: Image generators, autoencoders, and segmentation decoders may all reveal these unwanted patterns during upsampling.

Key takeaway: Checkerboard artifacts are mainly about uneven or learned spatial contributions, not a failure to increase image resolution.

Related: “What is a transposed convolution?” and “How does transposed convolution differ from interpolation-based upsampling?”

Research: Deconvolution and Checkerboard Artifacts — Odena, Dumoulin, and Olah (2016).