How does transposed convolution differ from interpolation-based upsampling?

Transposed convolution learns how to increase a feature map's spatial resolution. Interpolation-based upsampling uses a fixed resizing rule (such as nearest-neighbor or bilinear interpolation). A regular convolution can then be added after interpolation to learn how to refine the enlarged features.

How do they differ?

• Transposed convolution: Uses learned filters to produce a larger feature map in one operation. Kernel size, stride, and padding determine its output shape.
• Interpolation alone: Enlarges a feature map by copying nearby values (nearest-neighbor) or computing weighted averages (bilinear). The resize step has no trainable weights and normally preserves the channel count.
• Interpolation + convolution (resize-convolution): First resizes with a fixed rule, then uses a learned convolution to mix and refine features.

Practical example

Suppose the decoder input has shape `(1, 16, 4, 4)`: one image, 16 channels, and a 4×4 feature map. Both approaches below produce `(1, 8, 8, 8)`.

Option A — transposed convolution:

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

Option B — interpolate, then convolve:

nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), nn.Conv2d(16, 8, kernel_size=3, padding=1))

In Option B, interpolation expands 4×4 to 8×8 without changing the 16 channels; the following convolution learns to produce eight output channels.

Which should you use?

• Transposed convolution: Useful when learned upsampling is desired in a compact decoder layer.
• Interpolation + convolution: Gives explicit control over resizing and can reduce the risk of checkerboard artifacts caused by uneven overlap in some transposed convolutions.

Neither is always more accurate or faster. Compare output quality, memory, and latency for your architecture.

Remember: Both can increase resolution, but upsampling does not guarantee recovery of fine details lost earlier in the network.

Related: “What is a transposed convolution?” Next: “Why can transposed convolutions create checkerboard artifacts?”

References: PyTorch ConvTranspose2d, PyTorch Upsample, and Deconvolution and Checkerboard Artifacts.