# A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

arXiv: https://arxiv.org/abs/2307.05638 · HTML: https://mlsanity.com/paper/2307.05638/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 158 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 30 of 71)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)

## Built on by (most cited first, 22 of 22)

- [TiRex-2: Generalizing TiRex to Multivariate Data and Streaming](https://mlsanity.com/paper/2607.01204/) (2026)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [Neural Networks for Generating Better Local Optima in Topology Optimization](https://mlsanity.com/paper/2407.17957/) (2024)
- [Learning Actionable World Models for Industrial Process Control](https://mlsanity.com/paper/2503.01411/) (2025)
- [NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines](https://mlsanity.com/paper/2505.06333/) (2025)
- [Federated Learning from Molecules to Processes: A Perspective](https://mlsanity.com/paper/2506.18525/) (2025)
- [Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning](https://mlsanity.com/paper/2401.03070/) (2024)
- [Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions](https://mlsanity.com/paper/2406.06607/) (2024)
- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting](https://mlsanity.com/paper/2410.03805/) (2024)
- [On the Transfer of Knowledge in Quantum Algorithms](https://mlsanity.com/paper/2501.14120/) (2025)
- [Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning](https://mlsanity.com/paper/2504.17709/) (2025)
- [Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection](https://mlsanity.com/paper/2606.09874/) (2026)
- [On-device Anomaly Detection in Conveyor Belt Operations](https://mlsanity.com/paper/2411.10729/) (2024)
- [ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals](https://mlsanity.com/paper/2508.14689/) (2025)
- [Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection](https://mlsanity.com/paper/2511.15083/) (2025)
- [CAD 100K: A Comprehensive Multi-Task Dataset for Car Related Visual Anomaly Detection](https://mlsanity.com/paper/2604.09023/) (2026)
- [PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection](https://mlsanity.com/paper/2608.03926/) (2026)
- [FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Prediction](https://mlsanity.com/paper/2608.15727/) (2026)
- [Transfer Learning of Keystroke Dynamics for Cross-Device User Authentication](https://mlsanity.com/paper/2608.16334/) (2026)
- [Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study](https://mlsanity.com/paper/2608.22968/) (2026)
- [FlowTSFM: Turning Encoder Depth into Quantile Transport](https://mlsanity.com/paper/2609.13640/) (2026)

## Abstract (arXiv)

Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely interventions. Deep learning, with its capacity to discern non-trivial patterns within large datasets, plays a pivotal role in this process. Standard deep learning methods are suitable to solve a specific task given a specific type of data. During training, deep learning demands large volumes of labeled data. However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew. Deep transfer learning offers a solution to this problem. By leveraging knowledge from related tasks and accounting for variations in data distributions, the transfer learning framework solves new tasks with little or even no additional labeled data. The approach bypasses the need to retrain a model from scratch for every new setup and dramatically reduces the labeled data requirement. This survey first provides an in-depth review of deep transfer learning, examining the problem settings of transfer learning and classifying the prevailing deep transfer learning methods. Moreover, we delve into applications of deep transfer learning in the context of a broad spectrum of time series anomaly detection tasks prevalent in primary industrial domains, e.g., manufacturing process monitoring, predictive maintenance, energy management, and infrastructure facility monitoring. We discuss the challenges and limitations of deep transfer learning in industrial contexts and conclude the survey with practical directions and actionable suggestions to address the need to leverage diverse time series data for anomaly detection in an increasingly dynamic production environment.