Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and non-intrusive monitoring method in a complex manufacturing environment. However, labeling industry data for training is costly and time-consuming. Moreover, industry data is often scarce. In this study, we propose a novel adversarial domain adaptation (DA) approach to leverage abundant lab data to learn from scarce industry data, both labeled, for training a cutting-sound detection model. Rather than adapting the features from separate domains directly, we project them first into two separate latent spaces that jointly work as the feature space for learning domain-independent representations. We also analyze two different mechanisms for adversarial learning where the discriminator works as an adversary and a critic in separate settings, enabling our model to learn expressive domain-invariant and domain-ingrained features, respectively. We collected cutting sound data from multiple sensors in different locations, prepared datasets from lab and industry domain, and evaluated our learning models on them. Experiments showed that our models outperformed the multi-layer perceptron based vanilla domain adaptation models in labeling tasks on the curated datasets, achieving near 92%, 82% and 85% accuracy respectively for three different sensors installed in industry settings.
Department of Nuclear Engineering, North Carolina State University Burlington Engineering Laboratories, 2500 Stinson Drive, Raleigh, NC 27695（核工程系，北卡罗来纳州立大学）；Fuel Cycle Division, Oak Ridge National Laboratory 1 Bethel Valley Road, Oak Ridge, TN 37830（燃料循环部，橡树岭国家实验室）；Department of Nuclear Engineering, University of Tennessee, Knoxville Zeanah Engineering Complex, 863 Neyland Drive, Knoxville, TN 37916（核工程系，田纳西大学， Knoxville）
The use of ML in engineering has grown steadily to support a wide array of applications. Among these methods, deep neural networks have been widely adopted due to their performance and accessibility, but they require large, high-quality datasets. Experimental data are often sparse, noisy, or insufficient to build resilient data-driven models. Transfer learning, which leverages relevant data-abundant source domains to assist learning in data-scarce target domains, has shown efficacy. Parameter transfer, where pretrained weights are reused, is common but degrades under large domain shifts. Domai…
Image captioning systems have made substantial progress, largely due to the availability of curated datasets like Microsoft COCO or Vizwiz that have accurate descriptions of their corresponding images. Unfortunately, scarce availability of such cleanly labeled data results in trained algorithms producing captions that can be terse and idiosyncratically specific to details in the image. We propose a new technique, cooperative distillation that combines clean curated datasets with the web-scale automatically extracted captions of the Google Conceptual Captions dataset (GCC), which can have poor …
Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
Department of Aerospace Engineering, UC3M（航空航天工程系，UC3M）；Theoretical and Computational Aerodynamics Group, Flight Physics Department, INTA（理论与计算空气动力学组，飞行物理部门，INTA）
Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning
School of Engineering and Computer Science, Bern University of Applied Sciences, Biel, Switzerland（工程与计算机科学学院，伯恩应用科学大学，瑞士比尔）；Faculty of Informatics, Università della Svizzera italiana, Lugano, Switzerland（信息学院，瑞士意大利大学，瑞士卢加诺）；Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands（地质与遥感系，代尔夫特理工大学，荷兰代尔夫特）
Deep learning techniques have been increasingly applied to the natural sciences, e.g., for property prediction and optimization or material discovery. A fundamental ingredient of such approaches is the vast quantity of labelled data needed to train the model; this poses severe challenges in data-scarce settings where obtaining labels requires substantial computational or labor resources. Here, we introduce surrogate- and invariance-boosted contrastive learning (SIB-CL), a deep learning framework which incorporates three ``inexpensive'' and easily obtainable auxiliary information sources to ove…
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator
College of Engineering, Peking University（北京大学工学院）；Defense Innovation Institute, Chinese Academy of Military Science（中国人民解放军军事科学院国防创新研究院）；Institute of Advanced Structure Technology, Beijing Institute of Technology（北京理工大学先进结构技术研究院）
Prevailing methods for graphs require abundant label and edge information for learning. When data for a new task are scarce, meta-learning can learn from prior experiences and form much-needed inductive biases for fast adaption to new tasks. Here, we introduce G-Meta, a novel meta-learning algorithm for graphs. G-Meta uses local subgraphs to transfer subgraph-specific information and learn transferable knowledge faster via meta gradients. G-Meta learns how to quickly adapt to a new task using only a handful of nodes or edges in the new task and does so by learning from data points in other gra…
Cross-Learning from Scarce Data via Multi-Task Constrained Optimization
Department of Electrical Engineering, School of Engineering, Universidad de la República（电气工程系，工程学院，乌拉圭共和国大学）；Massachusetts Institute of Technology（麻省理工学院）；Department of Engineering, Universitat Pompeu Fabra（工程系，庞培法布拉大学）；Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）
Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion
School of Information Management, Central China Normal University（中央财经大学信息管理学院）；School of Cyber Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology（广东省脑启发式智能计算重点实验室，南方科技大学计算机科学与工程学院）
University of Pittsburgh（匹兹堡大学）；OpenAI；IBM Research（IBM研究院）；Google Research（谷歌研究院）；Stanford University（斯坦福大学）；Carnegie Mellon University（卡内基梅隆大学）；CIBO Technologies（CIBO科技）；University of Toronto（多伦多大学）；University of Cambridge（剑桥大学）
Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce as such libraries require years of concentrated effort by human specialists to be built. This is particularly challenging when applying large Transformer language models to tactic prediction, because the scaling of performance with respect to model size is quickly disrupted in the data-scarce, easily-overfitted regime. We propose PACT ({\bf P}roof {\bf A}rtifact {\bf C}o-{\bf T}raining), a general methodology for extracting abundant self-supervised data from kernel-level proof terms fo…
School of Information（信息学院）；University of Michigan（密歇根大学）
Machine learning on graph structured data has attracted much research interest due to its ubiquity in real world data. However, how to efficiently represent graph data in a general way is still an open problem. Traditional methods use handcraft graph features in a tabular form but suffer from the defects of domain expertise requirement and information loss. Graph representation learning overcomes these defects by automatically learning the continuous representations from graph structures, but they require abundant training labels, which are often hard to fulfill for graph-level prediction prob…
Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院金在哲人工智能研究生院）
As segmentation labels are scarce, extensive researches have been conducted to train segmentation networks with domain adaptation, semi-supervised or self-supervised learning techniques to utilize abundant unlabeled dataset. However, these approaches appear different from each other, so it is not clear how these approaches can be combined for better performance. Inspired by recent multi-domain image translation approaches, here we propose a novel segmentation framework using adaptive instance normalization (AdaIN), so that a single generator is trained to perform both domain adaptation and sem…
School of Mathematics, Sichuan University（四川大学数学学院）；School of Statistics and Data Science, Southwestern University of Finance and Economics（西南财经大学统计与数据科学学院）
Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data. However, traditional transfer learning methods often fail to generalize the pre-trained knowledge to the target task due to domain discrepancy. Self-supervised learning on graphs can increase the generalizabili…
In many computational science and engineering applications, the output of a system of interest corresponding to a given input can be queried at different levels of fidelity with different costs. Typically, low-fidelity data is cheap and abundant, while high-fidelity data is expensive and scarce. In this work we study the reinforcement learning (RL) problem in the presence of multiple environments with different levels of fidelity for a given control task. We focus on improving the RL agent's performance with multifidelity data. Specifically, a multifidelity estimator that exploits the cross-co…
