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Found 6 papers, 0 papers with code
Myocardial Infarction Detection from ECG: A Gramian Angular Field-based 2D-CNN Approach
Our proposed approach achieves an average classification accuracy of 99. 68\%, 99. 80\%, 99. 82\%, and 99. 84\% under GASF dataset with noise and baseline wander, GADF dataset with noise and baseline wander, GASF dataset with noise and baseline wander removed, and GADF dataset with noise and baseline wander removed, respectively.
Distribution Regularized Self-Supervised Learning for Domain Adaptation of Semantic Segmentation
Due to the domain shift, this decision boundary is unaligned in the target domain, resulting in noisy pseudo labels adversely affecting self-supervised domain adaptation.
FogAdapt: Self-Supervised Domain Adaptation for Semantic Segmentation of Foggy Images
We propose a self-entropy and multi-scale information augmented self-supervised domain adaptation method (FogAdapt) to minimize the domain shift in foggy scenes segmentation.
Ranked #1 on
Domain Adaptation
on SYNTHIA-to-FoggyCityscapes
MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutants
Such an attempt is a step toward addressing climate change's global challenge through appropriate monitoring and air quality tracking across a wider geographical region via affordable monitoring.
Twin-Net Descriptor: Twin Negative Mining With Quad Loss for Patch-Based Matching
Our results on Brown and HPatches datasets demonstrate Twin-Net's consistently better performance as well as better discriminatory and generalization capability as compared to the state-of-art.
Ranked #1 on
Patch Matching
on HPatches
MPNA: A Massively-Parallel Neural Array Accelerator with Dataflow Optimization for Convolutional Neural Networks
The state-of-the-art accelerators for Convolutional Neural Networks (CNNs) typically focus on accelerating only the convolutional layers, but do not prioritize the fully-connected layers much.
