Conv2DTranspose
Conv2DTranspose is a neural-network layer that applies a learned two-dimensional transposed convolution to increase the spatial resolution of feature maps.
Evolutionary Deep Learning: Genetic algorithms and neural networks
A Conv2DTranspose layer is a neural-network layer that performs a learned spatial upsampling operation commonly implemented as transposed convolution.
- How it is used
- Uses learned spatial upsampling to reconstruct images from latent representations in a variational autoencoder decoder.
- How deep it goes
- Explains decoder placement, stride-based expansion, filter reduction, and final output construction without deriving convolutional internals.
- Examples
- Demonstrates repeated decoder layers that expand feature maps, halve filters, and emit a sigmoid single-channel image.
- What you can do after
- Build it from scratch as the upsampling portion of a Keras variational autoencoder decoder.
Deep Learning with R, Third Edition
Conv2DTranspose is a Keras layer that applies a transposed two-dimensional convolution to increase the spatial resolution of feature representations.
- How it is used
- Expands compressed feature maps into segmentation masks with the same spatial dimensions as the input image.
- How deep it goes
- Explains learned upsampling through output-shape examples and a complete layer sequence, without mathematical derivation.
- Examples
- Uses a R/Keras segmentation model that expands (25, 25, 256) features into (200, 200, 3) pixel-class predictions.
- What you can do after
- Build a segmentation model in R/Keras by pairing strided Conv2D compression with Conv2DTranspose expansion.
Read how Deep Learning with R, Third Edition explains Conv2DTranspose →
Related concepts
- upsamplingimplements · Upsampling is a process that increases the number, density, or spatial or temporal resolution of samples or values in a dataset or representation by adding, duplicating, or estimating them.
- segmentation maskproduces · A segmentation mask is an image-shaped array that assigns each pixel or spatial element to a class, object, region, or corresponding segmentation value.
- convolutional layercontrasts with · A convolutional layer is a neural-network layer that applies shared learned filters to local regions of structured input to produce feature representations.
- feature pyramid networkused by · A feature pyramid network is a neural network architecture that combines semantically rich low-resolution features with spatially detailed high-resolution features across multiple scales.
- feature mapuses · A feature map is a spatially organized array of neural-network activations representing the presence or strength of learned features at different locations.
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