# LAMDA-SSL: Semi-Supervised Learning in Python

arXiv: https://arxiv.org/abs/2208.04610 · HTML: https://mlsanity.com/paper/2208.04610/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)

## Built on by (most cited first, 5 of 5)

- [Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts](https://mlsanity.com/paper/2309.10019/) (2023)
- [DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection](https://mlsanity.com/paper/2406.00345/) (2024)
- [Robust Semi-Supervised Learning in Open Environments](https://mlsanity.com/paper/2412.18256/) (2024)
- [Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification](https://mlsanity.com/paper/2404.17173/) (2024)
- [ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data](https://mlsanity.com/paper/2512.13228/) (2025)

## Abstract (arXiv)

LAMDA-SSL is open-sourced on GitHub and its detailed usage documentation is available at https://ygzwqzd.github.io/LAMDA-SSL/. This documentation introduces LAMDA-SSL in detail from various aspects and can be divided into four parts. The first part introduces the design idea, features and functions of LAMDA-SSL. The second part shows the usage of LAMDA-SSL by abundant examples in detail. The third part introduces all algorithms implemented by LAMDA-SSL to help users quickly understand and choose SSL algorithms. The fourth part shows the APIs of LAMDA-SSL. This detailed documentation greatly reduces the cost of familiarizing users with LAMDA-SSL toolkit and SSL algorithms.