Abstract: The paper proposes using alternative loss functions beyond mean squared error (MSE) for geoscience image regression tasks with neural networks. The ...

The demo sets up training parameters for the batch size (10), number of epochs to train (100), loss function (mean squared error), optimization algorithm (stochastic gradient descent) and learning ...

This project focuses on image compression using a convolutional autoencoder. The goal is to compress satellite images from the EuroSAT dataset into a compact representation and reconstruct them with ...

Credit must be given to the creator. Only noncommercial uses of the work are permitted. Comparison of the BNN predictions (red curve) and experimental values (black squares) of 159Tb for the ...

If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning models are developed.

kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1) but in your blog, you use kl_loss = - 0.5 * K.mean(1 + z_log_sigma - K.square(z ...