Search Results for author:
Found 7 papers, 2 papers with code
VDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variation
However, we empirically find that VDIP struggles with processing image details and tends to generate suboptimal results when the blur kernel is large.
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing
To mitigate the effects of label noise, learning with noisy labels (LNL) methods are designed to achieve better generalization performance.
CC-FedAvg: Computationally Customized Federated Averaging
Federated learning (FL) is an emerging paradigm to train model with distributed data from numerous Internet of Things (IoT) devices.
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework
In this paper, using a sequential Retinex decomposition strategy, we design a plug-and-play framework based on the Retinex theory for simultaneously image enhancement and noise removal.
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e. g., easy to hard) sequence.
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement
On the other hand, it enlarges the distances between local models, resulting in an aggregated global model with poor performance.
Color image segmentation based on a convex K-means approach
The proposed variational method uses a combination of $l_1$ and $l_2$ regularizers to maintain edge information of objects in images while overcoming the staircase effect.
