1.4k citations · 2.5k across the 13 of their papers we have counts for
20 papers
Revisiting Neural Scaling Laws in Language and Vision
Ibrahim Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai
The remarkable progress in deep learning in recent years is largely driven by improvements in scale, where bigger models are trained on larger datasets for longer schedules. To pre…
Better plain ViT baselines for ImageNet-1k
Lucas Beyer, Xiaohua Zhai, Alexander Kolesnikov
It is commonly accepted that the Vision Transformer model requires sophisticated regularization techniques to excel at ImageNet-1k scale data. Surprisingly, we find this is not the…
MLP-Mixer: An all-MLP Architecture for Vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov +9
Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this…
Revisiting the Calibration of Modern Neural Networks
Matthias Minderer, Josip Djolonga, Rob Romijnders +5
Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks. Many instances of miscalibration in modern neural networ…
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
Jessica Yung, Rob Romijnders, Alexander Kolesnikov +6
Before deploying machine learning models it is critical to assess their robustness. In the context of deep neural networks for image understanding, changing the object location, ro…
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
Vincent Dumoulin, Neil Houlsby, Utku Evci +4
Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art advances in each family are measured large…