# Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring

arXiv: https://arxiv.org/abs/2506.13909 · HTML: https://mlsanity.com/paper/2506.13909/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 81 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 30 of 30)

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- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
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- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [learn2learn: A Library for Meta-Learning Research](https://mlsanity.com/paper/2008.12284/) (2020)
- [Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning](https://mlsanity.com/paper/2206.03996/) (2022)
- [MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records](https://mlsanity.com/paper/1905.03218/) (2019)
- [Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning](https://mlsanity.com/paper/2402.15017/) (2024)
- [LibFewShot: A Comprehensive Library for Few-shot Learning](https://mlsanity.com/paper/2109.04898/) (2021)
- [BETA: A Large Benchmark Database Toward SSVEP-BCI Application](https://mlsanity.com/paper/1911.13045/) (2019)
- [Memory-Augmented Relation Network for Few-Shot Learning](https://mlsanity.com/paper/2005.04414/) (2020)
- [A Dataset for Greek Traditional and Folk Music: Lyra](https://mlsanity.com/paper/2211.11479/) (2022)
- [Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition](https://mlsanity.com/paper/2212.08952/) (2022)
- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [One-Way Prototypical Networks](https://mlsanity.com/paper/1906.00820/) (2019)

## Abstract (arXiv)

Few-shot learning (FSL) has shown promise in vision but remains largely unexplored for industrial time-series data, where annotating every new defect is prohibitively expensive. We present a systematic FSL study on screw-fastening process monitoring, using a 2,300-sample multivariate torque dataset that covers 16 uni- and multi-factorial defect types. Beyond benchmarking, we introduce a label-aware episodic sampler that collapses multi-label sequences into multiple single-label tasks, keeping the output dimensionality fixed while preserving combinatorial label information. Two FSL paradigms are investigated: the metric-based Prototypical Network and the gradient-based Model-Agnostic Meta-Learning (MAML), each paired with three backbones: 1D CNN, InceptionTime and the 341 M-parameter transformer Moment. On 10-shot, 3-way evaluation, the InceptionTime + Prototypical Network combination achieves a 0.944 weighted F1 in the multi-class regime and 0.935 in the multi-label regime, outperforming finetuned Moment by up to 5.3% while requiring two orders of magnitude fewer parameters and training time. Across all backbones, metric learning consistently surpasses MAML, and our label-aware sampling yields an additional 1.7% F1 over traditional class-based sampling. These findings challenge the assumption that large foundation models are always superior: when data are scarce, lightweight CNN architectures augmented with simple metric learning not only converge faster but also generalize better. We release code, data splits and pre-trained weights to foster reproducible research and to catalyze the adoption of FSL in high-value manufacturing inspection.