57 citations · 73 across the 8 of their papers we have counts for
4 papers · 1 filter
Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion
Emiel Hoogeboom, Thomas Mensink, Jonathan Heek +3
Latent diffusion models have become the popular choice for scaling up diffusion models for high resolution image synthesis. Compared to pixel-space models that are trained end-to-e…
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
Mostafa Dehghani, Basil Mustafa, Josip Djolonga +12
The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challeng…
Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets
Jonas Ngnawe, Marianne Abemgnigni Njifon, Jonathan Heek +1
Deep networks have achieved impressive results on a range of well-curated benchmark datasets. Surprisingly, their performance remains sensitive to perturbations that have little ef…
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Visio…