# Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing

arXiv: https://arxiv.org/abs/2401.08715 · HTML: https://mlsanity.com/paper/2401.08715/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 76 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [Learning to select data for transfer learning with Bayesian Optimization](https://mlsanity.com/paper/1707.05246/) (2017)
- [AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning](https://mlsanity.com/paper/1904.04153/) (2019)
- [Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media](https://mlsanity.com/paper/2010.01150/) (2020)
- [Using Similarity Measures to Select Pretraining Data for NER](https://mlsanity.com/paper/1904.00585/) (2019)
- [To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning](https://mlsanity.com/paper/2104.08078/) (2021)
- [Selective Weak Supervision for Neural Information Retrieval](https://mlsanity.com/paper/2001.10382/) (2020)
- [An Overview of Distance and Similarity Functions for Structured Data](https://mlsanity.com/paper/2002.07420/) (2020)
- [A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing](https://mlsanity.com/paper/2101.05605/) (2021)
- [Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study](https://mlsanity.com/paper/2305.11181/) (2023)
- [System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme](https://mlsanity.com/paper/2310.12286/) (2023)

## Built on by (most cited first, 3 of 3)

- [PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning](https://mlsanity.com/paper/2407.18569/) (2024)
- [A Dynamic Time Warping-Transfer Learning Approach to Transferring Knowledge in Stress-strain Behaviors from Polymers to Metals: An Affordable and Generalizable Additive Manufacturing Part Qualification Framework](https://mlsanity.com/paper/2512.08699/) (2025)
- [Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing](https://mlsanity.com/paper/2504.21317/) (2025)

## Abstract (arXiv)

Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to improve the modeling performance in target domains (e.g., new printings). Current applications use all accessible source data directly in TL with no regard to the similarity between source and target data. This paper proposes a systematic method to find appropriate subsets of source data based on similarities between the source and target datasets for a given set of limited target domain data. Such similarity is characterized by the spatial and model distance metrics. A Pareto frontier-based source data selection method is developed, where the source data located on the Pareto frontier defined by two similarity distance metrics are selected iteratively. The method is integrated into an instance-based TL method (decision tree regression model) and a model-based TL method (fine-tuned artificial neural network). Both models are then tested on several regression tasks in metal AM. Comparison results demonstrate that 1) the source data selection method is general and supports integration with various TL methods and distance metrics, 2) compared with using all source data, the proposed method can find a small subset of source data from the same domain with better TL performance in metal AM regression tasks involving different processes and machines, and 3) when multiple source domains exist, the source data selection method could find the subset from one source domain to obtain comparable or better TL performance than the model constructed using data from all source domains.