1.4k citations · 1.6k across the 6 of their papers we have counts for
12 papers
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…
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…
Are we done with ImageNet?
Lucas Beyer, Olivier J. Hénaff, Alexander Kolesnikov +2
Yes, and no. We ask whether recent progress on the ImageNet classification benchmark continues to represent meaningful generalization, or whether the community has started to overf…
Big Transfer (BiT): General Visual Representation Learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai +4
Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pr…
S4L: Self-Supervised Semi-Supervised Learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov +1
This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancin…