Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

An autoencoder is a specific type of a neural network, which is mainly designed to encode
the input into a compressed and meaningful representation and then decode it back such …

One of the major challenges in training text-to-image generation models is the need of a
large number of high-quality text-image pairs. While image samples are often easily …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

In image-to-image translation, each patch in the output should reflect the content of the
corresponding patch in the input, independent of domain. We propose a straightforward …

Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these
networks are trained on something new, they rapidly forget what was learned before. In the …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

We propose a novel method for unsupervised image-to-image translation, which
incorporates a new attention module and a new learnable normalization function in an end …

We advocate the use of implicit fields for learning generative models of shapes and
introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving …

Unsupervised image-to-image translation tasks aim to find a mapping between a source
domain X and a target domain Y from unpaired training data. Contrastive learning for …