Variational autoencoders (VAEs ... using their ability to explore the space of possible molecules. A convolutional autoencoder is a type of autoencoder that uses convolutional layers instead ...

making our autoencoder ‘variational’. It comprises an encoder, decoder, with the latent representation reparameterized in between. Encoder — The encoder consists of two convolutional layers, followed ...

files = [os.path.join(some_dir, file_i) for file_i in os.listdir(some_dir) if file_i.endswith('.jpg')] model.train_vae(files, input_shape, learning_rate=0.0001, batch ...

These methods train an autoencoder (AE) with only normal sound data and detect anomalies based on anomaly scores of actual samples. In this paper, we propose applying the convolutional variational ...

In this research, we investigate the performance of the variational autoencoder taking into account convolutional LSTM. To improve model performance, the model makes use of a latent space that follows ...

A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...