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Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher

arXiv 2212.13420 ↗First posted 27 Dec 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels and classification, allowing us to store only one model in memory instead of two. Our method attains similar performance to the Meta Pseudo Labels method while drastically reducing memory usage.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 27 Dec 2022.

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References · 18

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6 more
  1. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 510
  2. 2019Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillationcited by 497
  3. 2020Meta Pseudo Labelscited by 374
  4. 2016Adversarial Feature Learningcited by 350
  5. 2020An Overview of Deep Semi-Supervised Learningcited by 121
  6. 2021SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classificationcited by 68

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