---
title: "Who cites SSMBA: Self-Supervised Manifold — 60 citing papers"
description: "60 papers of the Elamur Research library cite «SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain…"
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---
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# Who cites «SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness»

60 papers of our library cite it (of the 141,840 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness»](https://md.research.elamur.ai/free/en/papers/ng-2020-ssmba-self-supervised-manifold-based/)

1. [**A Survey of Data Augmentation Approaches for NLP arXiv:2105.03075**Steven Y. Feng, Varun Gangal, Jason Wei, et al.*2021 · 937 citations*](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/) **[GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://md.research.elamur.ai/free/en/papers/yoo-2021-gpt3mix-leveraging-large-scale-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kang Min Yoo, Dongju Park, Jaewook Kang, et al.*2021 · 268 citations* **[Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://md.research.elamur.ai/free/en/papers/chung-2023-increasing-diversity-while-maintaining-accuracy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Chung, Ece Kamar, Saleema Amershi*2023 · 141 citations*

57 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/ng-2020-ssmba-self-supervised-manifold-based/) — SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness