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Negative Sampling in Semi-Supervised learning
John Chen · Vatsal Shah · Anastasios Kyrillidis

Wed Jul 15 08:00 AM -- 08:45 AM & Wed Jul 15 07:00 PM -- 07:45 PM (PDT) @

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)

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