# Unsupervised Learning via Meta-Learning

arXiv: https://arxiv.org/abs/1810.02334 · HTML: https://mlsanity.com/paper/1810.02334/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2018.

## Built on by (most cited first, 30 of 82)

- [Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples](https://mlsanity.com/paper/2104.13963/) (2021)
- [TabDPT: Scaling Tabular Foundation Models on Real Data](https://mlsanity.com/paper/2410.18164/) (2024)
- [Divide and Contrast: Self-supervised Learning from Uncurated Data](https://mlsanity.com/paper/2105.08054/) (2021)
- [MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption](https://mlsanity.com/paper/2103.16201/) (2021)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning](https://mlsanity.com/paper/2206.03996/) (2022)
- [Multiple Meta-model Quantifying for Medical Visual Question Answering](https://mlsanity.com/paper/2105.08913/) (2021)
- [STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables](https://mlsanity.com/paper/2303.00918/) (2023)
- [Unified Detection of Digital and Physical Face Attacks](https://mlsanity.com/paper/2104.02156/) (2021)
- [Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2103.04618/) (2021)
- [How to Train Your MAML to Excel in Few-Shot Classification](https://mlsanity.com/paper/2106.16245/) (2021)
- [Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning](https://mlsanity.com/paper/2301.03116/) (2023)
- [Self-Supervision Can Be a Good Few-Shot Learner](https://mlsanity.com/paper/2207.09176/) (2022)
- [Few-Shot Learning with a Strong Teacher](https://mlsanity.com/paper/2107.00197/) (2021)
- [In-context Exploration-Exploitation for Reinforcement Learning](https://mlsanity.com/paper/2403.06826/) (2024)
- [Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning](https://mlsanity.com/paper/2101.09499/) (2021)
- [Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning](https://mlsanity.com/paper/2303.00996/) (2023)
- [FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users](https://mlsanity.com/paper/2502.19312/) (2025)
- [On the Importance of Distractors for Few-Shot Classification](https://mlsanity.com/paper/2109.09883/) (2021)
- [The Effect of Diversity in Meta-Learning](https://mlsanity.com/paper/2201.11775/) (2022)
- [Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?](https://mlsanity.com/paper/2203.03059/) (2022)
- [SPeCiaL: Self-Supervised Pretraining for Continual Learning](https://mlsanity.com/paper/2106.09065/) (2021)
- [The Role of Global Labels in Few-Shot Classification and How to Infer Them](https://mlsanity.com/paper/2108.04055/) (2021)
- [Multi-level Second-order Few-shot Learning](https://mlsanity.com/paper/2201.05916/) (2022)
- [HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2301.03330/) (2023)
- [Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice](https://mlsanity.com/paper/2405.19313/) (2024)
- [Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation](https://mlsanity.com/paper/2109.15317/) (2021)
- [Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP](https://mlsanity.com/paper/2111.01322/) (2021)
- [Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning](https://mlsanity.com/paper/2303.11183/) (2023)
- [A Survey of the Self Supervised Learning Mechanisms for Vision Transformers](https://mlsanity.com/paper/2408.17059/) (2024)

## Abstract (arXiv)

A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics. Instead, we develop an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data. To do so, we construct tasks from unlabeled data in an automatic way and run meta-learning over the constructed tasks. Surprisingly, we find that, when integrated with meta-learning, relatively simple task construction mechanisms, such as clustering embeddings, lead to good performance on a variety of downstream, human-specified tasks. Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the embedding learned by four prior unsupervised learning methods.