ConvTranspose Output Calculator
Calculate the output shape produced by a transposed (deconvolution) layer.
Inputs
Output Size
16
Step by step
Output = (input−1)×stride − 2×pad + kernel + output_pad
(8−1)×2 − 2×1 + 4 + 0
= 16
How it works
Transposed convolution (deconvolution) upsamples feature maps. Its output size is (input-1)×stride - 2×padding + kernel_size + output_padding. It is used in decoder networks, GANs for image generation, and semantic segmentation for upsampling to full resolution.
Formula
ConvTranspose Output
output = (input - 1) × stride - 2 × padding + kernel_size + output_padding
- output_padding
- Extra padding added to one side of output
Frequently Asked Questions
Is transposed convolution the same as deconvolution?
Technically no — true deconvolution inverts a convolution. Transposed convolution is the gradient operation of convolution and is commonly (if imprecisely) called deconvolution in deep learning.
Why does transposed convolution cause checkerboard artifacts?
When stride doesn't evenly divide kernel size, overlapping regions receive unequal contributions, creating a checkerboard pattern. Using kernel_size divisible by stride (e.g., 4×4 with stride 2) helps.
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