Search Results for author:
Found 9 papers, 8 papers with code
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
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 challenged.
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
Milking CowMask for Semi-Supervised Image Classification
Using it to provide perturbations for semi-supervised consistency regularization, we achieve a state-of-the-art result on ImageNet with 10% labeled data, with a top-5 error of 8. 76% and top-1 error of 26. 06%.
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic impact.
S4L: Self-Supervised Semi-Supervised Learning
This work tackles the problem of semi-supervised learning of image classifiers.
Ranked #15 on
Semi-Supervised Image Classification
on ImageNet - 10% labeled data
(Top 5 Accuracy metric)
MixMatch: A Holistic Approach to Semi-Supervised Learning
Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
While practitioners have had an intuitive understanding of these observations, we do a comprehensive emperical analysis and demonstrate that: (1) the gains from SSL techniques over a fully-supervised baseline are smaller when trained from a pre-trained model than when trained from random initialization, (2) when the domain of the source data used to train the pre-trained model differs significantly from the domain of the target task, the gains from SSL are significantly higher and (3) some SSL methods are able to advance fully-supervised baselines (like Pseudo-Label).
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications.
Teacher-Student Curriculum Learning
We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on.
