# Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation

arXiv: https://arxiv.org/abs/2304.01404 · HTML: https://mlsanity.com/paper/2304.01404/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015)
- [Robust Super-Level Set Estimation using Gaussian Processes](https://mlsanity.com/paper/1811.09977/) (2018)
- [Multiscale Gaussian Process Level Set Estimation](https://mlsanity.com/paper/1902.09682/) (2019)
- [Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty](https://mlsanity.com/paper/1910.12043/) (2019)
- [Active learning for level set estimation under input uncertainty and its extensions](https://mlsanity.com/paper/1909.06064/) (2019)
- [Active learning for enumerating local minima based on Gaussian process derivatives](https://mlsanity.com/paper/1903.03279/) (2019)

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

- [Active Learning for Level Set Estimation Using Randomized Straddle Algorithms](https://mlsanity.com/paper/2408.03144/) (2024)
- [Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Prior](https://mlsanity.com/paper/2410.05660/) (2024)
- [An (ε,δ)-accurate level set estimation with a stopping criterion](https://mlsanity.com/paper/2503.20272/) (2025)
- [Dose-finding design based on level set estimation in phase I cancer clinical trials](https://mlsanity.com/paper/2504.09157/) (2025)

## Abstract (arXiv)

In material characterization, identifying defective areas on a material surface is fundamental. The conventional approach involves measuring the relevant physical properties point-by-point at the predetermined mesh grid points on the surface and determining the area at which the property does not reach the desired level. To identify defective areas more efficiently, we propose adaptive mapping methods in which measurement resources are used preferentially to detect the boundaries of defective areas. We interpret this problem as an active-learning (AL) of the level set estimation (LSE) problem. The goal of AL-based LSE is to determine the level set of the physical property function defined on the surface with as small number of measurements as possible. Furthermore, to handle the situations in which materials with similar specifications are repeatedly produced, we introduce a transfer learning approach so that the information of previously produced materials can be effectively utilized. As a proof-of-concept, we applied the proposed methods to the red-zone estimation problem of silicon wafers and demonstrated that we could identify the defective areas with significantly lower measurement costs than those of conventional methods.