Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Autoencoder networks are unsupervised approaches aiming at combining generative and
representational properties by learning simultaneously an encoder-generator map. Although …

Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for
deep generative models. However, learning a VAE from data poses still unanswered …

The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …

Representation learning with small labeled data have emerged in many problems, since the
success of deep neural networks often relies on the availability of a huge amount of labeled …

While recent developments in text-to-image generative models have led to a suite of high-
performing methods capable of producing creative imagery from free-form text, there are …

Thanks to the recent development of deep generative models, it is becoming possible to
generate high-quality images with both fidelity and diversity. However, the training of such …

Humans can only interact with part of the surrounding environment due to biological
restrictions. Therefore, we learn to reason the spatial relationships across a series of …

Heterogeneous face recognition (HFR) refers to matching cross-domain faces and plays a
crucial role in public security. Nevertheless, HFR is confronted with challenges from large …