1k citations · 1.7k across the 4 of their papers we have counts for
4 papers
Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman +6
Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set s…
LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu +13
Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate la…
ResNet strikes back: An improved training procedure in timm
Ross Wightman, Hugo Touvron, Hervé Jégou
The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture…
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai +3
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and seman…