# The Omniglot challenge: a 3-year progress report

arXiv: https://arxiv.org/abs/1902.03477 · HTML: https://mlsanity.com/paper/1902.03477/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2019.

## Built on by (most cited first, 30 of 54)

- [Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans](https://mlsanity.com/paper/2110.04994/) (2021)
- [Data Distributional Properties Drive Emergent In-Context Learning in Transformers](https://mlsanity.com/paper/2205.05055/) (2022)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [The mechanistic basis of data dependence and abrupt learning in an in-context classification task](https://mlsanity.com/paper/2312.03002/) (2023)
- [Opening up Open-World Tracking](https://mlsanity.com/paper/2104.11221/) (2021)
- [Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach](https://mlsanity.com/paper/2310.18677/) (2023)
- [MEWL: Few-shot multimodal word learning with referential uncertainty](https://mlsanity.com/paper/2306.00503/) (2023)
- [Is attention required for ICL? Exploring the Relationship Between Model Architecture and In-Context Learning Ability](https://mlsanity.com/paper/2310.08049/) (2023)
- [Frozen Feature Augmentation for Few-Shot Image Classification](https://mlsanity.com/paper/2403.10519/) (2024)
- [Towards Task Sampler Learning for Meta-Learning](https://mlsanity.com/paper/2307.08924/) (2023)
- [Anchor function: a type of benchmark functions for studying language models](https://mlsanity.com/paper/2401.08309/) (2024)
- [Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?](https://mlsanity.com/paper/2301.11722/) (2023)
- [Interactive Visual Reasoning under Uncertainty](https://mlsanity.com/paper/2206.09203/) (2022)
- [Compositional diversity in visual concept learning](https://mlsanity.com/paper/2305.19374/) (2023)
- [Hacking Task Confounder in Meta-Learning](https://mlsanity.com/paper/2312.05771/) (2023)
- [Set-to-Sequence Methods in Machine Learning: a Review](https://mlsanity.com/paper/2103.09656/) (2021)
- [Drawing out of Distribution with Neuro-Symbolic Generative Models](https://mlsanity.com/paper/2206.01829/) (2022)
- [On-device Online Learning and Semantic Management of TinyML Systems](https://mlsanity.com/paper/2405.07601/) (2024)
- [Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers](https://mlsanity.com/paper/2601.20796/) (2026)
- [Learning from One and Only One Shot](https://mlsanity.com/paper/2201.08815/) (2022)
- [Adaptive Prototypical Networks](https://mlsanity.com/paper/2211.12479/) (2022)
- [A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques](https://mlsanity.com/paper/2302.08571/) (2023)
- [TinyReptile: TinyML with Federated Meta-Learning](https://mlsanity.com/paper/2304.05201/) (2023)
- [Meta-learning of Pooling Layers for Character Recognition](https://mlsanity.com/paper/2103.09528/) (2021)
- [Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks](https://mlsanity.com/paper/2406.06079/) (2024)
- [Embedding Adaptation is Still Needed for Few-Shot Learning](https://mlsanity.com/paper/2104.07255/) (2021)
- [Offline Handwritten Amharic Character Recognition Using Few-shot Learning](https://mlsanity.com/paper/2210.00275/) (2022)
- [Computing with Categories in Machine Learning](https://mlsanity.com/paper/2303.04156/) (2023)
- [Guided Transfer Learning](https://mlsanity.com/paper/2303.16154/) (2023)
- [Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP](https://mlsanity.com/paper/2306.04410/) (2023)

## Abstract (arXiv)

Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not meant to be the final word on Omniglot; we hoped that the community would build on our work and develop new approaches. In the time since, we have been pleased to see wide adoption of the dataset. There has been notable progress on one-shot classification, but researchers have adopted new splits and procedures that make the task easier. There has been less progress on the other four tasks. We conclude that recent approaches are still far from human-like concept learning on Omniglot, a challenge that requires performing many tasks with a single model.