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John Chen 0002

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UAI Conference 2022 Conference Paper

Stackmix: a complementary mix algorithm

  • John Chen 0002
  • Samarth Sinha
  • Anastasios Kyrillidis

Techniques combining multiple images as input/output have proven to be effective data augmentations for training convolutional neural networks. In this paper, we present StackMix: each input is presented as a concatenation of two images, and the label is the mean of the two one-hot labels. On its own, StackMix rivals other widely used methods in the “Mix” line of work. More importantly, unlike previous work, significant gains across a variety of benchmarks are achieved by combining StackMix with existing Mix augmentation, effectively mixing more than two images. E. g. , by combining StackMix with CutMix, test error in the supervised setting is improved across a variety of settings over CutMix, including 0. 8% on ImageNet, 3% on Tiny ImageNet, 2% on CIFAR-100, 0. 5% on CIFAR-10, and 1. 5% on STL-10. Similar results are achieved with Mixup. We further show that gains hold for robustness to common input corruptions and perturbations at varying severities with a 0. 7% improvement on CIFAR-100-C, by combining StackMix with AugMix over AugMix. On its own, improvements with StackMix hold across different number of labeled samples on CIFAR-100, maintaining approximately a 2% gap in test accuracy –down to using only 5% of the whole dataset– and is effective in the semi-supervised setting with a 2% improvement with the standard benchmark Pi-model. Finally, we perform an extensive ablation study to better understand the proposed methodology.

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ICML Conference 2020 Conference Paper

Negative Sampling in Semi-Supervised learning

  • John Chen 0002
  • Vatsal Shah
  • Anastasios Kyrillidis

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.

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