Search Results for author:
Found 14 papers, 7 papers with code
DomainLab: A modular Python package for domain generalization in deep learning
DomainLab is a modular Python package for training user specified neural networks with composable regularization loss terms.
Federated Learning for Data and Model Heterogeneity in Medical Imaging
In this paper, we exploit the data and model heterogeneity simultaneously, and propose a method, MDH-FL (Exploiting Model and Data Heterogeneity in FL) to solve such problems to enhance the efficiency of the global model in FL.
Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology
Here, we train a robust CNN for WBC classification by addressing cross-domain data imbalance and domain shifts.
Real Image Super-Resolution using GAN through modeling of LR and HR process
The current existing deep image super-resolution methods usually assume that a Low Resolution (LR) image is bicubicly downscaled of a High Resolution (HR) image.
RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network
Modern digital cameras and smartphones mostly rely on image signal processing (ISP) pipelines to produce realistic colored RGB images.
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution
The existing SR methods have limited performance due to a fixed degradation settings, i. e. usually a bicubic downscaling of low-resolution (LR) image.
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution
We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions, which is inspired by the recent success of CycleGAN in the image-to-image translation applications.
Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution
Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data.
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations
Single Image Super-Resolution (SISR) aims to generate a high-resolution (HR) image of a given low-resolution (LR) image.
