Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria -- informativeness, representativeness, and diversity -- have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness.Comment: Int'l Joint Conf. on Neural Networks (IJCNN), Glasgow, UK, July 202

A comprehensive survey on deep active learning and its applications in medical image analysis

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. Additionally, we also highlight active learning works that are specifically tailored to medical image analysis. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis.Comment: Paper List on Github: https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysi

Exploring Active 3D Object Detection from a Generalization Perspective

To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance. Our empirical study, however, suggests that mainstream uncertainty-based and diversity-based active learning policies are not effective when applied in the 3D detection task, as they fail to balance the trade-off between point cloud informativeness and box-level annotation costs. To overcome this limitation, we jointly investigate three novel criteria in our framework Crb for point cloud acquisition - label conciseness}, feature representativeness and geometric balance, which hierarchically filters out the point clouds of redundant 3D bounding box labels, latent features and geometric characteristics (e.g., point cloud density) from the unlabeled sample pool and greedily selects informative ones with fewer objects to annotate. Our theoretical analysis demonstrates that the proposed criteria align the marginal distributions of the selected subset and the prior distributions of the unseen test set, and minimizes the upper bound of the generalization error. To validate the effectiveness and applicability of \textsc{Crb}, we conduct extensive experiments on the two benchmark 3D object detection datasets of KITTI and Waymo and examine both one-stage (\textit{i.e.}, \textsc{Second}) and two-stage 3D detectors (i.e., Pv-rcnn). Experiments evidence that the proposed approach outperforms existing active learning strategies and achieves fully supervised performance requiring $1\%$ and $8\%$ annotations of bounding boxes and point clouds, respectively. Source code: https://github.com/Luoyadan/CRB-active-3Ddet.Comment: To appear in ICLR 202

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives-for application-based opportunities, with emphasis on those that address big data with geospatial components

Parametric active learning techniques for 3D hand pose estimation

Active learning (AL) has recently gained popularity for deep learning (DL) models due to efficient and informative sampling, especially when the models require large-scale datasets. The DL models designed for 3D-HPE demand accurate and diverse large-scale datasets that are time-consuming, costly and require experts. This thesis aims to explore AL primarily for the 3D hand pose estimation (3D-HPE) task for the first time. The thesis delves directly into an AL methodology customised for 3D-HPE learners to address this. Because predominantly the learners are regression-based algorithms, a Bayesian approximation of a DL architecture is presented to model uncertainties. This approximation generates data and model- dependent uncertainties that are further combined with the data representativeness AL function, CoreSet, for sampling. Despite being the first work, it creates informative samples and minimal joint errors with less training data on three well-known depth datasets. The second AL algorithm continues to improve the selection following a new trend of parametric samplers. Precisely, this is proceeded task-agnostic with a Graph Convolutional Network (GCN) to offer higher order of representations between labelled and unlabelled data. The newly selected unlabelled images are ranked based on uncertainty or GCN feature distribution. Another novel sampler extends this idea, and tackles encountered AL issues, like cold-start and distribution shift, by training in a self-supervised way with contrastive learning. It shows leveraging the visual concepts from labelled and unlabelled images while attaining state-of-the-art results. The last part of the thesis brings prior AL insights and achievements in a unified parametric-based sampler proposal for the multi-modal 3D-HPE task. This sampler trains multi-variational auto-encoders to align the modalities and provide better selection representation. Several query functions are studied to open a new direction in deep AL sampling.Open Acces

GAINING SCIENTIFIC AND ENGINEERING INSIGHT INTO GROUND MOTION SIMULATION THROUGH MACHINE LEARNING AND APPROXIMATE MODELING APPROACHES

This dissertation presents a series of methods for gaining scientific and engineering insight into earthquake ground motion simulation in three areas: synthetic validation, attenuation modeling, and nonlinear effects estimation. First, I present guidelines to reduce the number of metrics used to evaluate the goodness-of-fit (GOF) between ground motion synthetics and recorded data in an application independent framework. Validation of ground motion simulations is mostly done using metrics that are user- or application-biased. Comparisons between synthetics from regional scale ground motion simulations and recorded data from past earthquakes provide opportunities to approach the problems using data-driven methods. I used a combination of semi-supervised and supervised learning methods to prioritize GOF metrics based on a large dataset and was able to identify the response spectra- and energy integral-based metrics as the most dominant ones for estimating the accuracy of simulations. Second, in two related studies, I present an application of customized solutions used to characterize attenuation (quality factor Q) with respect to shear wave velocity (Vs) for individual stations within a simulation. I used an artificial neural network as a supervised learning method to develop pseudo-simulators to be used in an optimization process to estimate the dominant Vs range for each station, and thus estimate Q. Using parameters such as peak ground acceleration, response spectra, the area under the velocity signal\u27s envelope and the peak ground velocity, I show it is possible to improve the optimization process to locate the most accurate Q parameters. Last, I present an approximate model to estimate nonlinear soil effects in ground motion simulations by implementing an approach inspired in the equivalent linear method. This implementation is done for three-dimensional simulations, from source to site, without any pre- or post-processing of data. Fully nonlinear ground motion simulation methods need comprehensive input data and are computationally challenging. The approach implemented can be used to estimate first-order nonlinear soil effects (e.g., deamplificaiton and resonant frequency shift) effectively. I calibrate the approach using idealized models