Recent image generation models such as Stable Diffusion have exhibited an impressive
ability to generate fairly realistic images starting from a simple text prompt. Could such …

Deep generative models are becoming increasingly powerful, now generating diverse high
fidelity photo-realistic samples given text prompts. Have they reached the point where …

Current generative networks are increasingly proficient in generating high-resolution
realistic images. These generative networks, especially the conditional ones, can potentially …

Recently proposed large-scale text-to-image generative models such as DALLE 2,
Midjourney, and StableDiffusion can generate high-quality and realistic images from users' …

Recent significant advances in text-to-image models unlock the possibility of training vision
systems using synthetic images potentially overcoming the difficulty of collecting curated …

As clean ImageNet accuracy nears its ceiling, the research community is increasingly more
concerned about robust accuracy under distributional shifts. While a variety of methods have …

Recent text-to-image generation models have shown promising results in generating high-
fidelity photo-realistic images. Though the results are astonishing to human eyes, how …

How do two sets of images differ? Discerning set-level differences is crucial for
understanding model behaviors and analyzing datasets yet manually sifting through …

Does progress on ImageNet transfer to real-world datasets? We investigate this question by
evaluating ImageNet pre-trained models with varying accuracy (57%-83%) on six practical …

In this work, we introduce a self-supervised feature representation learning framework
DreamTeacher that utilizes generative networks for pre-training downstream image …