Search Results for author: Avital Oliver

Found 7 papers, 6 papers with code

Milking CowMask for Semi-Supervised Image Classification

2 code implementations26 Mar 2020 Geoff French, Avital Oliver, Tim Salimans

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%.

Classification General Classification +1

When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets

no code implementations13 Dec 2018 Hong-Yu Zhou, Avital Oliver, Jianxin Wu, Yefeng Zheng

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).

pseudo label Transfer Learning

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

7 code implementations NeurIPS 2018 Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow

However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications.

Teacher-Student Curriculum Learning

3 code implementations1 Jul 2017 Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman

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.

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