Timezone: »
We introduce Negative Sampling in Semi-Supervised Learning (NS^3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL). NS^3L is motivated by the success of negative sampling/contrastive estimation. We demonstrate that adding the NS^3L loss to state-of-the-art SSL algorithms, such as the Virtual Adversarial Training (VAT), significantly improves upon vanilla VAT and its variant, VAT with Entropy Minimization. By adding the NS^3L loss to MixMatch, the current state-of-the-art approach on semi-supervised tasks, we observe significant improvements over vanilla MixMatch. We conduct extensive experiments on the CIFAR10, CIFAR100, SVHN and STL10 benchmark datasets. Finally, we perform an ablation study for NS3L regarding its hyperparameter tuning.
Author Information
John Chen (Rice University)
Vatsal Shah (University of Texas at Austin)
Anastasios Kyrillidis (Rice University)
More from the Same Authors
-
2021 : Mitigating deep double descent by concatenating inputs »
John Chen · Qihan Wang · Anastasios Kyrillidis -
2023 : Adaptive Federated Learning with Auto-Tuned Clients »
J. Lyle Kim · Mohammad Taha Toghani · Cesar Uribe · Anastasios Kyrillidis -
2021 Workshop: Beyond first-order methods in machine learning systems »
Albert S Berahas · Anastasios Kyrillidis · Fred Roosta · Amir Gholaminejad · Michael Mahoney · Rachael Tappenden · Raghu Bollapragada · Rixon Crane · J. Lyle Kim -
2020 Workshop: Beyond first order methods in machine learning systems »
Albert S Berahas · Amir Gholaminejad · Anastasios Kyrillidis · Michael Mahoney · Fred Roosta -
2019 Poster: Compressing Gradient Optimizers via Count-Sketches »
Ryan Spring · Anastasios Kyrillidis · Vijai Mohan · Anshumali Shrivastava -
2019 Oral: Compressing Gradient Optimizers via Count-Sketches »
Ryan Spring · Anastasios Kyrillidis · Vijai Mohan · Anshumali Shrivastava