As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to
displace many in the professional artist community. In particular, models can learn to mimic …

In this paper, we identify and characterize the emerging area of representation engineering
(RepE), an approach to enhancing the transparency of AI systems that draws on insights …

The topical domain generalization (DG) problem asks trained models to perform well on an
unseen target domain with different data statistics from the source training domains. In …

Deep learning systems typically suffer from catastrophic forgetting of past knowledge when
acquiring new skills continually. In this paper, we emphasize two dilemmas, representation …

Although generative adversarial networks (GANs) have made significant progress in face
synthesis, there lacks enough understanding of what GANs have learned in the latent …

Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity
image synthesis, there lacks enough understanding of how GANs are able to map a latent …

We systematically study a wide variety of generative models spanning semantically-diverse
image datasets to understand and improve the feature extractors and metrics used to …

In recent months a machine learning based free software tool has made it easy to create
believable face swaps in videos that leaves few traces of manipulation, in what are known …