Progressive Learning Method Research Articles

Variational masking progressive learning method for multi-rate industrial processes soft sensor

BackgroundA deep progressive learning method for PET image reconstruction named deep progressive reconstruction (DPR) method was developed and presented in previous works. It has been shown in previous study that the DPR with one-third duration can maintain the image quality as OSEM with standard dose (3.7 MBq/kg). Subsequent studies have shown we can reduce the administered activity of 18F-FDG by up to 2/3 in a real-world deployment with DPR. The aim of this study is to assess the impact of the use of DPR on Deauville score (DS) and clinical interpretation of PET/CT in patients with lymphoma.MethodsA total of 87 lymphoma patients (age, 45.1 ± 14.9 years) who underwent 18F-FDG PET imaging for during or post-treatment follow-up from November 2020 to February 2024 were prospectively enrolled. The patients were randomly assigned to two groups, including the 1/3 standard dose group and the standard dose group. Forty-four patients were injected with 1/3 standard dose (1.23 MBq/kg) and scanned for 6 min per bed and were reconstructed: ordered-subsets expectation maximization (OSEM) with 6 min per bed (OSEM_6 min_1/3), OSEM_2 min_1/3 and DPR_2 min_1/3. Forty-three patients were scanned according to the standard protocol (3.7 MBq/kg) and were reconstructed: OSEM with 2 min per bed (OSEM_2 min_full), OSEM_40 s_full and DPR_40 s_full. Additionally, the conventional 5-point scale measurement analysis was performed and DS for lymphoma were determined in different groups. Wilcoxon signed-rank test was used to compare the mean values of liver SUVmax and mediastinal blood pool (MBP) SUVmax in each group. Likert scale and DS were evaluated using Wilcoxon signed rank test.ResultsThe patients with OSEM_6 min_1/3 and DPR_2 min_1/3 showed good image quality with 5(5,5) and 5(4,5) of Likert scoring, as well as the patients with OSEM_2 min_full and DPR_40 s_full. No significant difference was found between the OSEM_6 min_1/3 and DPR_2 min_1/3 groups in terms of liver SUVmax and MBP SUVmax (P = 0.452 and 0.430), as well as the patients with OSEM_2 min_full and DPR_40 s_full (P = 0.105 and 0.638). No significant difference was found between the OSEM_6 min_1/3 and DPR_2 min_1/3 groups in terms of lesion SUVmax (P = 0.080). There was a significant differences in lesion SUVmax between OSEM-2 min_full with DPR-40 s_full (P = 0.027). The DS results were consistent (100%) between OSEM-6 min_1/3 with DPR_2 min_1/3, and between OSEM-2 min_full with DPR-40 s_full, respectively.ConclusionsDPR reconstruction demonstrated feasibility in reducing PET injection dose or scanning time, while ensuring the preservation of image quality and DS for during or post-treatment follow-up patients with lymphoma.

Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unified framework using a novel progressive learning approach comprised of three phases: i) a coarse learning phase for concept representation, ii) a fine learning phase for action generation, and iii) an imaginary learning phase for domain adaptation. Overall, this approach leads to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-shot domain-adaptive imitation learning</i> framework. We use robotic pouring as an example task to evaluate its effectiveness. Our results show that the method has several advantages over contemporary end-to-end imitation learning approaches, including an improved success rate for task execution and more efficient training for deep imitation learning. In addition, the generalizability to new domains is improved, as demonstrated here with novel backgrounds, target containers, and granule combinations in the experiment. We believe that the proposed method is broadly applicable to various industrial or domestic applications that involve deep imitation learning for robotic manipulation, and where the target scenarios are diverse and human demonstration data is limited. For project video, please check our website:. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to develop a progressive learning framework, which can be used for both service and industrial robots to learn from human demonstrations, and then transfer the learned skill to different scenarios with ease. We use the robotic pouring task as an example to demonstrate the effectiveness of our proposed method, since pouring is an essential skill for service robots to assist humans’ daily lives, and can benefit robot automation in wet-lab industries. The aim of this research is to enable robots to obtain visuomotor skills (such as the pouring skill), and accomplish the tasks with a high success rate using our proposed progressive learning method. We conducted experiments to show that the proposed method has good performance, high data efficiency and evident generalizability. This is significant for intelligent robots working in various practical applications.

RGB‐guided hyperspectral image super‐resolution with deep progressive learning

This article focuses on unsupervised methods for optical aerial image change detection. Existing unsupervised change detection techniques are mainly categorized as patch-based methods and transfer-learning-based methods. However, the first type ignores the spatial information in the images, and the second type may introduce new errors due to knowledge extracted from additional datasets. To effectively tackle these problems, we propose an unsupervised progressive learning framework (UPLF). We first use original estimated change maps as the labeled samples and choose the reliable regions from samples to train the network. We then propose a progressive learning method to expand the reliable labeled region. Briefly, we apply a label selection filter to filter out incorrect change information from the regions to help rectify incorrect labeling in the regions. This leads to a more reliable labeled region and thus, in turn, more accurate detection results. Compared with the patch-based and transfer-learning-based unsupervised techniques, our method takes the entire map as the training sample to avoid the problem associated with using small patches; moreover, our iterative and progressive methods further enhance the change detection performance without involving external knowledge. Indeed, based on our experimental results on the real datasets, the proposed method demonstrates highly competitive performance compared with the state-of-the-art.

Human beings tend to learn unknown knowledge in a gradual process, from the basic to the complex. Based on this point, we propose a progressive learning method for producing service strategies according to requests, with a hierarchical priori knowledge and reinforcement learning. Service strategy aims to guide how to perform home services and takes into consideration the relationship between actions and objects in home environment. In this paper, strategy generation is regarded as a text generation problem in question answering (QA). Firstly, a hierarchical priori knowledge with service-object correlation at the bottom and action-object correlation at the top is constructed to assist the understanding on the relationship of objects and actions in service strategies. Service-object correlation guides how to select proper objects with the correct order, while action-object correlation associates actions in strategies according to selected objects. Based on the hierarchical priori knowledge, a progressive learning method is proposed to make the model produce effective strategies with a sequential cognition, from service-object correlation (objects) to action-object correlation (actions). After that, reinforcement learning is employed to enhance the progressive guidance, by designing rewards in terms of the hierarchical priori knowledge. Finally, the proposed method is tested with both comparative experiments and ablation studies, and the experimental results demonstrate the superiority in producing comprehensive and logical strategies, indicating that the progressive learning method in our paper can further improve the QA performance.

Dynamic video mix-up for cross-domain action recognition

An analytic layer-wise deep learning framework with applications to robotics

A progressive learning method on unknown protocol behaviors

A knowledge-guide hierarchical learning method for long-tailed image classification

Supervised methods are increasingly used to identify cell populations in single-cell data. Yet, current methods are limited in their ability to learn from multiple datasets simultaneously, are hampered by the annotation of datasets at different resolutions, and do not preserve annotations when retrained on new datasets. The latter point is especially important as researchers cannot rely on downstream analysis performed using earlier versions of the dataset. Here, we present scHPL, a hierarchical progressive learning method which allows continuous learning from single-cell data by leveraging the different resolutions of annotations across multiple datasets to learn and continuously update a classification tree. We evaluate the classification and tree learning performance using simulated as well as real datasets and show that scHPL can successfully learn known cellular hierarchies from multiple datasets while preserving the original annotations. scHPL is available at https://github.com/lcmmichielsen/scHPL.

A progressive learning method with an instrumental variable and bond-valence vector sums was used to improve the bandgap prediction precision.

As demonstrated earlier, the learning accuracy of the single-layer-feedforward-network (SLFN) is generally far lower than expected, which has been a major bottleneck for many applications. In fact, for some large real problems, it is accepted that after tremendous learning time (within finite epochs), the network output error of SLFN will stop or reduce increasingly slowly. This report offers an extreme learning machine (ELM)-based learning method, referred to as the parent-offspring progressive learning method. The proposed method works by separating the data points into various parts, and then multiple ELMs learn and identify the clustered parts separately. The key advantages of the proposed algorithms as compared to the traditional supervised methods are twofold. First, it extends the ELM learning method from a single neural network to a multinetwork learning system, as the proposed multiELM method can approximate any target continuous function and classify disjointed regions. Second, the proposed method tends to deliver a similar or much better generalization performance than other learning methods. All the methods proposed in this paper are tested on both artificial and real datasets.

A Progressive Learning Method for Symbol Recognition

A graph-based image annotation framework

Stable Adaptive Control of Non-Collocated Systems Using the Progressive Learning Method