# One-shot skill assessment in high-stakes domains with limited data via meta learning

arXiv: https://arxiv.org/abs/2301.00812 · HTML: https://mlsanity.com/paper/2301.00812/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples](https://mlsanity.com/paper/1903.03096/) (2019)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [Meta-Learning Representations for Continual Learning](https://mlsanity.com/paper/1905.12588/) (2019)
- [Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks](https://mlsanity.com/paper/1802.08774/) (2018)
- [Video-based surgical skill assessment using 3D convolutional neural networks](https://mlsanity.com/paper/1903.02306/) (2019)
- [Deep Learning on Small Datasets without Pre-Training using Cosine Loss](https://mlsanity.com/paper/1901.09054/) (2019)
- [How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks](https://mlsanity.com/paper/2009.05835/) (2020)
- [One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video](https://mlsanity.com/paper/2103.12988/) (2021)
- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning](https://mlsanity.com/paper/2203.09589/) (2022)
- [Automated Human Cell Classification in Sparse Datasets using Few-Shot Learning](https://mlsanity.com/paper/2107.13093/) (2021)

## Built on by (most cited first, 4 of 4)

- [Exploring Pre-training Across Domains for Few-Shot Surgical Skill Assessment](https://mlsanity.com/paper/2509.09327/) (2025)
- [CoRe-DA: Contrastive Regression for Unsupervised Domain Adaptation in Surgical Skill Assessment](https://mlsanity.com/paper/2603.29666/) (2026)
- [Cognitive-Motor Integration in Assessing Bimanual Motor Skills](https://mlsanity.com/paper/2404.10889/) (2024)
- [Deep Learning for Video-Based Assessment of Endotracheal Intubation Skills](https://mlsanity.com/paper/2404.11727/) (2024)

## Abstract (arXiv)

Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5% in one-shot and 99.9% in few-shot settings for simulated tasks and 89.7% for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.