To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

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 …

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

We introduce GAUDI, a generative model capable of capturing the distribution of complex
and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle …

Recent advances in computer vision have shown promising results in image generation.
Diffusion probabilistic models have generated realistic images from textual input, as …

The field of neural rendering has witnessed significant progress with advancements in
generative models and differentiable rendering techniques. Though 2D diffusion has …

Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance
of using more data in GAN training. Yet it is expensive to collect data in many domains such …

Conditional GANs are at the forefront of natural image synthesis. The main drawback of such
models is the necessity for labeled data. In this work we exploit two popular unsupervised …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Anomalies are rare and anomaly detection is often therefore framed as One-Class
Classification (OCC), ie trained solely on normalcy. Leading OCC techniques constrain the …