# Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection

arXiv: https://arxiv.org/abs/2204.01620 · HTML: https://mlsanity.com/paper/2204.01620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing](https://mlsanity.com/paper/2101.00509/) (2021)
- [Regularization-based Continual Learning for Fault Prediction in Lithium-Ion Batteries](https://mlsanity.com/paper/2107.03336/) (2021)
- [Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data](https://mlsanity.com/paper/2106.04920/) (2021)
- [Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models](https://mlsanity.com/paper/2107.03324/) (2021)

## Abstract (arXiv)

Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer's results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches' applicability caused by its results' reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.