# Self-supervised Neural Architecture Search

arXiv: https://arxiv.org/abs/2007.01500 · HTML: https://mlsanity.com/paper/2007.01500/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Self-supervised Cross-silo Federated Neural Architecture Search](https://mlsanity.com/paper/2101.11896/) (2021)
- [Contrastive Self-supervised Neural Architecture Search](https://mlsanity.com/paper/2102.10557/) (2021)
- [Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision](https://mlsanity.com/paper/2403.05064/) (2024)
- [Self-Supervised Neural Architecture Search for Imbalanced Datasets](https://mlsanity.com/paper/2109.08580/) (2021)
- [Towards Self-supervised and Weight-preserving Neural Architecture Search](https://mlsanity.com/paper/2206.04125/) (2022)
- [NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks](https://mlsanity.com/paper/2302.00059/) (2023)
- [Self-Supervised Neural Architecture Search for Multimodal Deep Neural Networks](https://mlsanity.com/paper/2512.24793/) (2025)

## Abstract (arXiv)

Neural Architecture Search (NAS) has been used recently to achieve improved performance in various tasks and most prominently in image classification. Yet, current search strategies rely on large labeled datasets, which limit their usage in the case where only a smaller fraction of the data is annotated. Self-supervised learning has shown great promise in training neural networks using unlabeled data. In this work, we propose a self-supervised neural architecture search (SSNAS) that allows finding novel network models without the need for labeled data. We show that such a search leads to comparable results to supervised training with a "fully labeled" NAS and that it can improve the performance of self-supervised learning. Moreover, we demonstrate the advantage of the proposed approach when the number of labels in the search is relatively small.