"train_dataset = MNIST(dataset_path, transform=mnist_transform, train=True, download=True)\n", "test_dataset = MNIST(dataset_path, transform=mnist_transform, train ...
This repository implements a Denoising Variational Autoencoder (DVAE) based on the U-Net architecture to remove various types of image noise. The model is trained on a subset of ImageNet data with ...
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 ...
Abstract: Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate ...
Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in a low-dimensional ...
Abstract: Recent advancements in food image recognition have underscored its importance in dietary monitoring, which promotes a healthy lifestyle and aids in the prevention of diseases such as ...
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