Abstract: Accurate automated extraction of coseismic deformation from synthetic aperture radar (SAR) data can be challenging owing to interference from inherent atmospheric noise. Particularly, the ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Same as traditional autoencoders, VAE architecture has two parts: an encoder and a decoder. Traditional AE models map inputs into a latent-space vector and reconstruct the output from this vector. VAE ...

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the ...

Abstract: A complex-valued autoencoder neural network ca-pable of compressing & denoising radio frequency signals with arbitrary model scaling is proposed. Complex-valued time sam-ples received with ...

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...