Hierarchical Active Learning Research Articles

Hierarchical Active Learning with Label Proportions on Data Regions

Hierarchical Active Learning with Label Proportions on Data Regions

Hierarchical active learning for defect localization in 3D systems

Aim: Advanced sensing and imaging is capable to retrieve rich information of complex systems, which can be integrated with underlying physics to develop a personalized simulation framework to discern system internal property for defect localization. Simulation-based defect localization involves modeling the heterogeneous 3D physical systems and calibrating spatial-varying model parameters. In this paper, we aim to develop an effective simulation-based active learning framework for defect localization in complex 3D systems. Methods: We develop a Hierarchical Gaussian Process (GP)-based Active Learning (HGPAL) framework to estimate the spatial-varying model parameters for reliable defect localization in complex 3D systems. We first develop a GP regression model on the 2D embedding of a 3D geometry to capture the geometric information. Second, we propose a Hierarchical GP-based active learning approach composed of master GP and sub-GP modeling to account for the system heterogeneity and further estimate the spatial-varying model parameter for defect localization. Results: We evaluate the performance of the proposed HGPAL framework in a 3D body-heart system to identify and localize infarct in the heart. Numerical experiments demonstrate the effectiveness of our HGPAL framework for 3D defect localization. Conclusion: This study successfully developed and tested an HGPAL framework for defect localization in complex 3D systems. The results suggest that this approach can efficiently and accurately localize defects in 3D systems, demonstrating its potential for practical applications in precision engineering system design and healthcare treatment planning.

Hierarchical Active Learning With Qualitative Feedback on Regions.

Learning classification models in practice usually requires numerous labeled data for training. However, instance-based annotation can be inefficient for humans to perform. In this article, we propose and study a new type of human supervision that is fast to perform and useful for model learning. Instead of labeling individual instances, humans provide supervision to data regions, which are subspaces of the input data space, representing subpopulations of data. Since labeling now is performed on a region level, 0/1 labeling becomes imprecise. Thus, we design the region label to be a qualitative assessment of the class proportion, which coarsely preserves the labeling precision but is also easy for humans to do. To identify informative regions for labeling and learning, we further devise a hierarchical active learning process that recursively constructs a region hierarchy. This process is semisupervised in the sense that it is driven by both active learning strategies and human expertise, where humans can provide discriminative features. To evaluate our framework, we conducted extensive experiments on nine datasets as well as a real user study on a survival analysis of colorectal cancer patients. The results have clearly demonstrated the superiority of our region-based active learning framework against many instance-based active learning methods.

Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams.

Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA’s performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies.

Development of Creativity Education Method for First-year University Students with Hierarchical Active Learning

Development of Creativity Education Method for First-year University Students with Hierarchical Active Learning

Hierarchical Active Learning with Overlapping Regions.

Learning of classification models from real-world data often requires substantial human effort devoted to instance annotation. As this process can be very time-consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. To address this problem we explore a new type of human feedback - region-based feedback. Briefly, a region is defined as a hypercubic subspace of the input data space and represents a subpopulation of data instances; the region's label is a human assessment of the class proportion of the data subpopulation. By using learning from label proportions algorithms one can learn instance-based classifiers from such labeled regions. In general, the key challenge is that there can be infinite many regions one can define and query in a given data space. To minimize the number and complexity of region-based queries, we propose and develop a hierarchical active learning solution that aims at incrementally building a concise hierarchy of regions. Furthermore, to avoid building a possibly class-irrelevant region hierarchy, we further propose to grow multiple different hierarchies in parallel and expand those more informative hierarchies. Through experiments on numerous data sets, we demonstrate that methods using region-based feedback can learn very good classifiers from very few and simple queries, and hence are highly effective in reducing human annotation effort needed for building classification models.

Using convolutional neural networks incorporating hierarchical active learning for target-searching in large-scale remote sensing images

ABSTRACTSatellite images are important information sources of the earth environment. Automatic classification of satellite images has always been an important research topic. With the recent advancement of deep learning, the convolutional neural network (CNN) approach has shown great potential in object detection in high resolution images. However, insufficient labelled samples and constrained input image sizes have limited the wide application of CNN for remote sensing. In this study, the Hierarchical Active Learning (HAL) framework is proposed by incorporating transfer learning, tile map service (TMS), and active learning to enable effective scene classification with very few manually labelled samples. A case study of vehicle detection with HAL has been conducted and shows that HAL can achieve the accuracy of more than 80% with only 50 training samples for a large area. Moreover, its ability to extend to incorporation of different TMS image sources and CNN models makes it useful for various object detection tasks.

Hierarchical Active Learning with Proportion Feedback on Regions.

Learning of classification models in practice often relies on human annotation effort in which humans assign class labels to data instances. As this process can be very time-consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. To solve this problem, instead of soliciting instance-based annotation we explore region-based annotation as the feedback. A region is defined as a hyper-cubic subspace of the input feature space and it covers a subpopulation of data instances that fall into this region. Each region is labeled with a number in [0,1] (in binary classification setting), representing a human estimate of the positive (or negative) class proportion in the subpopulation. To learn a classifier from region-based feedback we develop an active learning framework that hierarchically divides the input space into smaller and smaller regions. In each iteration we split the region with the highest potential to improve the classification models. This iterative process allows us to gradually learn more refined classification models from more specific regions with more accurate proportions. Through experiments on numerous datasets we demonstrate that our approach offers a new and promising active learning direction that can outperform existing active learning approaches especially in situations when labeling budget is limited and small.

Hierarchical Active Learning with Group Proportion Feedback.

Learning of classification models in practice often relies on nontrivial human annotation effort in which humans assign class labels to data instances. As this process can be very time consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. In this work we solve this problem by exploring a new approach that actively learns classification models from groups, which are subpopulations of instances, and human feedback on the groups. Each group is labeled with a number in [0,1] interval representing a human estimate of the proportion of instances with one of the class labels in this subpopulation. To form the groups to be annotated, we develop a hierarchical active learning framework that divides the whole population into smaller subpopulations, which allows us to gradually learn more refined models from the subpopulations and their class proportion labels. Our extensive experiments on numerous datasets show that our method is competitive and outperforms existing approaches for reducing the human annotation cost.