# Learning to Learn in a Semi-Supervised Fashion

arXiv: https://arxiv.org/abs/2008.11203 · HTML: https://mlsanity.com/paper/2008.11203/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions](https://mlsanity.com/paper/2206.02780/) (2022)
- [OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data](https://mlsanity.com/paper/2107.08943/) (2021)
- [ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation](https://mlsanity.com/paper/2102.06328/) (2021)
- [RankingMatch: Delving into Semi-Supervised Learning with Consistency Regularization and Ranking Loss](https://mlsanity.com/paper/2110.04430/) (2021)

## Abstract (arXiv)

To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval. Our learning scheme exploits the idea of leveraging information from labeled to unlabeled data. Instead of fitting the associated class-wise similarity scores as most meta-learning algorithms do, we propose to derive semantics-oriented similarity representations from labeled data, and transfer such representation to unlabeled ones. Thus, our strategy can be viewed as a self-supervised learning scheme, which can be applied to fully supervised learning tasks for improved performance. Our experiments on various tasks and settings confirm the effectiveness of our proposed approach and its superiority over the state-of-the-art methods.