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Found 7 papers, 4 papers with code
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm.
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks.
Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles
The single-particle cryo-EM field faces the persistent challenge of preferred orientation, lacking general computational solutions.
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Current approaches aim at generating synthesized training data from unpaired samples by exploring the relationship between the corrupted and clean data.
Unsupervised Deep Learning Meets Chan-Vese Model
The Chan-Vese (CV) model is a classic region-based method in image segmentation.
Learning From Unpaired Data: A Variational Bayes Approach
Collecting the paired training data is a difficult task in practice, but the unpaired samples broadly exist.
An Unsupervised Deep Learning Approach for Real-World Image Denoising
In the real-world case, the noise distribution is so complex that the simplified additive white Gaussian (AWGN) assumption rarely holds, which significantly deteriorates the Gaussian denoisers' performance.
