upsampler

neural networks use upsamplers to increase the spatial resolution of feature maps.

the image processing pipeline includes a convolutional upsampler for upscaling.

our model employs a learned upsampler to reconstruct high-resolution outputs.

the upsampler block transforms low-resolution features into dense representations.

transposed convolution is commonly used as an upsampler in deep learning architectures.

the upsampler layer doubles the spatial dimensions of the input tensor.

training the upsampler requires balancing reconstruction quality and computational efficiency.

the sub-pixel upsampler achieves high-resolution output through efficient channel rearrangement.

adaptive upsamplers can learn task-specific upscaling kernels.

the residual upsampler architecture preserves fine details during upscaling.

the depth-to-space upsampler rearranges channel data into spatial dimensions.

our upsampler implementation reduces artifacts in generated images.