4 accepted papers
Federated learning (FL) often suffers from performance degradation due to key challenges such as data heterogeneity and communication constraints. To address these limitations, we present a novel FL framework called FedWSQ, which integrates weight standardization (WS) and the proposed distribution-a…
Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images. Having long been proven to be effective in image restoration tasks, a single lane of encoder-decoder architecture overlooks the characteristic of deblurring, where a blurry…
Recently, a generative adversarial network (GAN)-based method employing the coarse-to-fine network with the contextual attention module (CAM) has shown outstanding results in image inpainting. However, this method requires numerous computational resources due to its two-stage process for feature enc…
Recently developed object detectors employ a convolutional neural network (CNN) by gradually increasing the number of feature layers with a pyramidal shape instead of using a featurized image pyramid. However, the different abstraction levels of the CNN feature layers often limit the detection perfo…