Tata Steel Defect Detection Project

该项目将探索在缺陷分类系统的训练中使用主动学习框架，以识别从成像系统观察到的表面缺陷。要探索的问题将包括是否可以使用人在回路系统来利用领域专家知识和迭代机器学习来产生一个强大而准确的系统，用于识别各种复杂的类。该项目还将研究监督和无监督机器学习的基本方法，以确定哪些方法为标签问题提供可靠但有效的方法。需要回答的一个关键问题是，观察结果之间的关系是否可以用来在标记数据时指导和告知用户，以及这是否反映在系统的性能中。 使用的方法：该项目将首先实施一个标签系统，并听取一组最终用户和以前文献的意见。标签系统将允许他们接收图像，并提供一种有效的方法来标记一小部分观察到的数据，然后训练监督机器学习模型来预测剩余图像的标签。然后，用户将进入一个主动学习循环，在那里他们更新标签，重新训练模型并审查预测。然后重复此操作，直到用户满意或满足另一标准。然后，该项目将寻求实现一个底层数据结构，该结构允许表示观察到的样本之间的关系。然后，该数据结构将用于训练机器学习模型，该模型不仅考虑图像的外观，还考虑样本之间的潜在关系。基于图的深度学习方法将用于概括问题的基本领域。然后，这种基于图的方法可以用于预测和生成模型，也可以用于将问题可视化呈现给域用户以供进一步检查。新颖的内容：项目的新奇来自于对图像之间潜在关系的理解，并利用它们来指导和加强主动学习框架。它应该确定主动学习社区的关键方法，还应该提供利用基于图形的信息进行可视化和解释模型活动的方法。

The project will explore the use of active learning frameworks in the training of a defect classification system for identifying surface defects observed from imaging systems. The questions to explore will include whether a human-in-the-loop system can be used to utilise domain expert knowledge and iterative machine learning to produce a robust and accurate system for recognising a wide range of complex classes. The project will also look into underlying methods of supervised and unsupervised machine learning, in order to identify which approaches provide reliable but efficient approaches for the labelling problem. One key question to be answered is whether the relationships between the observations can be utilised to guide and inform the user during their labelling of the data, and whether this is then reflected in the system's performance. The approaches used:The project will first implement a labelling system with input from a group of end users and previous literature. The labelling system will allow them to take in images and provide an effective method for labelling a small selection of the observed data, before then training supervised machine learning models to predict labels for the remaining images. The user will then enter into an active learning loop where they update the labels, retrain the model and review the predictions. This then repeats until the user is satisfied, or another criterion is met. The project will then look to implement an underlying data-structure which allows relationships between the observed samples to be represented. This data-structure will then be used to allow a machine learning model to be trained which takes not only the appearance of the image into consideration, but also the underlying relationships between the samples. Graph-based deep learning approaches will be used to then generalise the underlying domain of the problem. This graph-based approach can then be used for both predictive and generative models, and also in the visual presentation of the problem back to the domain user for further inspection. Novel content:The project novelty comes from understanding the underlying relationships between the images, and using them to guide and strengthen the active learning framework. It should identify key approaches in the active learning community, and should also provide methods for utilising the graph-based information for visualisation and interpretation of model activity.