# Becoming the Expert - Interactive Multi-Class Machine Teaching

arXiv: https://arxiv.org/abs/1504.07575 · HTML: https://mlsanity.com/paper/1504.07575/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2015.

## Built on by (most cited first, 12 of 12)

- [Iterative Teaching by Label Synthesis](https://mlsanity.com/paper/2110.14432/) (2021)
- [Iterative Teaching by Data Hallucination](https://mlsanity.com/paper/2210.17467/) (2022)
- [Iterative Teacher-Aware Learning](https://mlsanity.com/paper/2110.00137/) (2021)
- [Learning Data Teaching Strategies Via Knowledge Tracing](https://mlsanity.com/paper/2111.07083/) (2021)
- [Making a Bird AI Expert Work for You and Me](https://mlsanity.com/paper/2112.02747/) (2021)
- [ML-Based Teaching Systems: A Conceptual Framework](https://mlsanity.com/paper/2305.07681/) (2023)
- [Transferring Domain Knowledge with (X)AI-Based Learning Systems](https://mlsanity.com/paper/2406.01329/) (2024)
- [L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection and Enhancement](https://mlsanity.com/paper/2412.09765/) (2024)
- [Visual Knowledge Tracing](https://mlsanity.com/paper/2207.10157/) (2022)
- [Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity](https://mlsanity.com/paper/2110.11778/) (2021)
- [One-shot Machine Teaching: Cost Very Few Examples to Converge Faster](https://mlsanity.com/paper/2212.06416/) (2022)
- [FathomVerse: A community science dataset for ocean animal discovery](https://mlsanity.com/paper/2412.01701/) (2024)

## Abstract (arXiv)

Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important and representative images first, followed by less important images later - or not at all. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithm chooses, online, which labeled images from a teaching set should be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student's ability and progress, based on their correct and incorrect answers, produces better 'experts'. We present results using real human participants across several varied and challenging real-world datasets.