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Found 6 papers, 1 papers with code
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution
Recent Reference-Based image super-resolution (RefSR) has improved SOTA deep methods introducing attention mechanisms to enhance low-resolution images by transferring high-resolution textures from a reference high-resolution image.
Ranked #1 on
Image Super-Resolution
on Sun80 - 4x upscaling
How to train your VAE
Variational Autoencoders (VAEs) have become a cornerstone in generative modeling and representation learning within machine learning.
Attentive VQ-VAE
We present a novel approach to enhance the capabilities of VQ-VAE models through the integration of a Residual Encoder and a Residual Pixel Attention layer, named Attentive Residual Encoder (AREN).
Hadamard Layer to Improve Semantic Segmentation
The performance's improvement can be explained by the Hadamard layer forcing the network to produce an internal encoding of the classes so that all bins are active.
AxonNet: A self-supervised Deep Neural Network for Intravoxel Structure Estimation from DW-MRI
Our methods are based on a proposed parameter representation suitable for the problem.
Deep neural network for fringe pattern filtering and normalisation
We propose a new framework for processing Fringe Patterns (FP).
