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Found 4 papers, 1 papers with code
cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation
Specifically, our experiments show that training convolutional neural networks (CNNs) with our cDVGAN-generated data improves the detection of samples embedded in detector noise beyond the synthetic data from other state-of-the-art GAN models.
DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics
This paper presents a novel approach to simulating fixed-length time-domain signals using a three-player Wasserstein Generative Adversarial Network (WGAN), called DVGAN, that includes an auxiliary discriminator that discriminates on the derivatives of input signals.
Simulation-Based Optimization of User Interfaces for Quality-Assuring Machine Learning Model Predictions
QA for ML (QA4ML) interfaces require users to view a large amount of data and perform many interactions to correct errors made by the ML model.
Monitoring Spatial Sustainable Development: semi-automated analysis of Satellite and Aerial Images for Energy Transition and Sustainability Indicators
The cross-site evaluation was furthermore carried out twice: deep learning models trained on he Netherlands were evaluated on Germany and vice versa.
