Transfer Learning Framework for 3-D Electromagnetic Applications

This study proposes an antenna simulation framework powered by graphics processing units (GPUs) based on an open-source electromagnetic (EM) simulation software (gprMax) for machine learning applications of antenna design and optimization. Furthermore, it compares the simulation results with those obtained through commercial EM software. The proposed software framework for machine learning and surrogate model applications will produce antenna data sets consisting of a large number of antenna simulation results using GPUs. Although machine learning methods can attain the optimum solutions for many problems, they are known to be data-hungry and require a great deal of samples for the training stage of the algorithms. However, producing a sufficient number of training samples in EM applications within a limited time is challenging due to the high computational complexity of EM simulations. Therefore, GPUs are utilized in this study to simulate a large number of antennas with predefined or random antenna shape parameters to produce data sets. Moreover, this study also compares various machine learning and deep learning models in terms of antenna parameter estimation performance. This study demonstrates that an entry-level GPU substantially outperforms a high-end CPU in terms of computational performance, while a high-end gaming GPU can achieve around 18 times more computational performance compared to a high-end CPU. Moreover, it is shown that the open-source EM simulation software can deliver similar results to those obtained via commercial software in the simulation of microstrip antennas when the spatial resolution of the simulations is sufficiently fine.

We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable knowledge for recognising a target object class from the background given very limited training target samples. Unlike existing transfer learning techniques, our method does not assume that auxiliary data are labelled, nor the relationships between target and auxiliary classes are known a priori. Our unsupervised transfer learning is formulated by a novel kernel adaptation transfer (KAT) learning framework, which aims to (a) extract general knowledge about how more structured objects are visually distinctive from cluttered background regardless object class, and (b) more importantly, perform selective transfer of knowledge extracted from the auxiliary data to minimise negative knowledge transfer suffered by existing methods. The effectiveness and efficiency of the proposed approach is demonstrated by performing one-class object recognition (object vs. background) task using the Caltech256 dataset.

This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-independent ASR system in a unified sequence-to-sequence (S2S) architecture with a shared vocabulary among all languages. During adaptation, we perform LM fusion transfer, where an external LM is integrated into the decoder network of the attention-based S2S model in the whole adaptation stage, to effectively incorporate linguistic context of the target language. We also investigate various seed models for transfer learning. Experimental evaluations using the IARPA BABEL data set show that LM fusion transfer improves performances on all target five languages compared with simple transfer learning when the external text data is available. Our final system drastically reduces the performance gap from the hybrid systems.

The purpose of transfer learning is to utilize the knowledge gained from the existing (source) domain to enhance the performance on a distinct but related (target) domain. Existing works on transfer learning are not capable of optimizing different quality measures (components) such as minimizing the marginal distribution, minimizing the conditional distribution, maximizing the target domain variance, modeling the manifold by utilizing the common geometric properties in the source as well as the target domain at the same time. Moreover, existing transfer learning methods use conventional approaches to determine the appropriate values of their parameters, which is very hectic and time-consuming. Therefore, in order to overcome the drawbacks of existing approaches, we propose a Particle Swarm Optimization based Parameter Selection Approach for Unsupervised Discriminant Analysis (UDATL-PSO) in transfer learning framework. In UDATL-PSO, all the quality measures are considered at the same time, as well as the PSO approach has been used to select the best values of their parameters. Extensive experiments on various transfer learning tasks show that the proposed method has a significant influence on state-of-the-art methods.

Evaluation of transfer learning in data-driven methods in the assessment of unconventional resources

Rapid thermal processing (RTP) is an important process in the fabrication of semiconductor devices. It is difficult to achieve temperature uniformity control of the wafer in RTP since the system is a highly nonlinear process with strong spatial distribution. In this study, a transfer learning-based three-dimensional (3D) fuzzy multivariable control scheme is proposed for the temperature uniformity control of an RTP system. In difference to the traditional expert-knowledge based design, a two-level framework of transfer learning methodology is constructed to design the 3D fuzzy multivariable controller (3D FMC) with the help of a multi-output support vector regression (M-SVR). The 3D FMC defines a qualitative spatial fuzzy structure that will be transferred to the M-SVR. On the other hand, the structure parameters of the M-SVR will be learned from data and transferred to design quantitative parameters of the 3D FMC. Under the framework of transfer learning, the control laws (e.g. human control experience) hidden in spatio-temporal data can be extracted and formulated back into multi-output 3D fuzzy rules. The proposed method provides an effective integration of the spatial fuzzy inference and the transfer learning for 3D FLC design. The newly developed method is applied to the temperature uniformity control of a rapid thermal chemical vapor deposition (RTCVD) system at the set temperature 1000K, and the maximum non-uniformity along the wafer radius is close to 1K.

Deep learning models face a trust deficit due to poor generalization and 'black-box' interpretability. Conventional transfer learning, reliant on statistical alignment, fails to guarantee physical plausibility. We propose a Physics-Constrained Transfer Learning (PCTL) framework based on the core insight that while raw signals vary, intrinsic physical fault patterns—like harmonic structures in the envelope spectrum—remain domain-invariant. Its key innovation is a 'diagnosis-verification-feedback' loop where an external, rule-based PCV module quantifies the consistency between a diagnosis and its physical evidence. This consistency score guides a confidence predictor, compelling the model's confidence to align with physical rationality. Extensive experiments show PCTL achieves superior accuracy and embeds reliable self-assessment, demonstrated by a strong correlation between predicted confidence and physical consistency. This research offers a new paradigm for developing intelligent diagnostic systems that are accurate, physically interpretable, and trustworthy.

The concept of transfer learning has received a great deal of concern and interest throughout the last decade. Selecting an ideal representational framework for instances of various domains to minimize the divergence among source and target domains is a fundamental research challenge in representative transfer learning. The domain adaptation approach is designed to learn more robust or higher-level features, required in transfer learning. This paper presents a novel transfer learning framework that employs a marginal probability-based domain adaptation methodology followed by a deep autoencoder. The proposed frame adapts the source and target domain by plummeting distribution deviation between the features of both domains. Further, we adopt the deep neural network process to transfer learning and suggest a supervised learning algorithm based on encoding and decoding layer architecture. Moreover, we have proposed two different variants of the transfer learning techniques for classification, which are termed as (i) Domain adapted transfer learning with deep autoencoder-1 (D-TLDA-1) using the linear regression and (ii) Domain adapted transfer learning with deep autoencoder-2 (D-TLDA-2) using softmax regression. Simulations have been conducted with two popular real-world datasets: ImageNet datasets for image classification problem and 20_Newsgroups datasets for text classification problem. Experimental findings have established and the resulting improvements in accuracy measure of classification shows the supremacy of the proposed D-TLDA framework over prominent state-of-the-art machine learning and transfer learning approaches.

Over the past few years, deep neural networks have made significant processes in denoising low-dose CT images. A trained denoising network, however, may not generalize very well to different dose levels, which follows from the dose-dependent noise distribution. To address this practically, a trained network requires re-training to be applied to a new dose level, which limits the generalization abilities of deep neural networks for clinical applications. This article introduces a deep learning approach that does not require re-training and relies on a transfer learning strategy. More precisely, the transfer learning framework utilizes a progressive denoising model, where an elementary neural network serves as a basic denoising unit. The basic units are then cascaded to successively process towards a denoising task; i.e. the output of one network unit is the input to the next basic unit. The denoised image is then a linear combination of outputs of the individual network units. To demonstrate the application of this transfer learning approach, a basic CNN unit is trained using the Mayo low- dose CT dataset. Then, the linear parameters of the successive denoising units are trained using a different image dataset, i.e. the MGH low-dose CT dataset, containing CT images that were acquired at four different dose levels. Compared to a commercial iterative reconstruction approach, the transfer learning framework produced a substantially better denoising performance.

Dear Editor, This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning (DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1], [2]. The proposed transfer learning framework includes the design of neural network architecture, curriculum transfer learning (CTL) and strategy distillation. Experimental results demonstrate that our framework enables DMARL models to converge faster while improving the final performance.

Accurate prediction of land surface temperature (LST) is critical for remote sensing applications, yet remains hindered by in situ data scarcity, limited input variables, and regional variability. To address these limitations, we introduce a three-stage strategic fine-tuning-based transfer learning (SFTL) framework that integrates a large simulated dataset (430 K samples), in situ measurements from the Heihe and Huailai regions in China, and high-resolution imagery from the GF5-02 Visible and Infrared Multispectral Imager (VIMI). The key novelty of this study is the combination of large-scale simulation, an engineered humidity-sensitive feature, and multiple parameter-efficient tuning strategies—full, head, gradual, adapter, and low-rank adaptation (LoRA)—within a unified transfer-learning framework for cross-site LST estimation. In Stage 1, pre-training with 5-fold cross-validation on the simulated dataset produced strong baseline models, including Random Forest (RF), Light Gradient Boosting Machine (LGBM), Deep Neural Network (DNN), Transformer (TrF), and Convolutional Neural Network (CNN). In Stage 2, strategic fine-tuning was conducted under two cross-regional scenarios—Heihe-to-Huailai and Huailai-to-Heihe—and model transfer for tree-based learners. Fine-tuning achieved competitive in-domain performance while materially improving cross-site transfer. When trained on Huailai and tested on Heihe, DNN-gradual attained RMSE 2.89 K (R2 ≈ 0.96); when trained on Heihe and tested on Huailai, TrF-head achieved RMSE 3.34 K (R2 ≈ 0.94). In Stage 3, sensitivity analyses confirmed stability across IQR multipliers of 1.0–1.5, with <1% RMSE variation across models and sites, indicating robustness against outliers. Additionally, application to real GF5-02 VIMI imagery demonstrated that the best SFTL configurations aligned with spatiotemporal in situ observations at both sites, capturing the expected spatial gradients. Overall, the proposed SFTL framework—anchored in cross-validation, strategic fine-tuning, and large-scale simulation—outperforms the widely used Split-Window (SW) algorithm (Huailai: RMSE = 3.64 K; Heihe: RMSE = 4.22 K) as well as direct-training Machine Learning (ML) models, underscoring their limitations in modeling complex regional variability.

Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task. However, most existing works assume both tasks are sampled from a stationary task distribution, thereby leading to the sub-optimal performance for dynamic tasks drawn from a non-stationary task distribution in real scenarios. To bridge this gap, in this paper, we study a more realistic and challenging transfer learning setting with dynamic tasks, i.e., source and target tasks are continuously evolving over time. We theoretically show that the expected error on the dynamic target task can be tightly bounded in terms of source knowledge and consecutive distribution discrepancy across tasks. This result motivates us to propose a generic meta-learning framework L2E for modeling the knowledge transferability on dynamic tasks. It is centered around a task-guided meta-learning problem with a group of meta-pairs of tasks, based on which we are able to learn the prior model initialization for fast adaptation on the newest target task. L2E enjoys the following properties: (1) effective knowledge transferability across dynamic tasks; (2) fast adaptation to the new target task; (3) mitigation of catastrophic forgetting on historical target tasks; and (4) flexibility in incorporating any existing static transfer learning algorithms. Extensive experiments on various image data sets demonstrate the effectiveness of the proposed L2E framework.

Deep learning has attracted increasing attention in brain imaging, but many neuroimaging data samples are small and fail to meet the training data requirements to optimize performance. In this study, we propose a deep transfer learning network based on Optimal Mass Transport (OMTNet) for 3D brain image classification using MRI scans from the UK Biobank. The major contributions of the OMTNet method include: a way to map 3D surface-based vertex-wise brain shape metrics, including cortical thickness, surface area, curvature, sulcal depth, and subcortical radial distance and surface Jacobian determinant metrics, onto 2D planar images for each MRI scan based on area-preserving mapping. Such that some popular 2D convolution neural networks pretrained on the ImageNet database, such as ResNet152 and DenseNet201, can be used for transfer learning of brain shape metrics. We used a score-fusion strategy to fuse all shape metrics and generate an ensemble classification. We tested the approach in a classification task conducted on 26k participants from the UK Biobank, using body mass index (BMI) thresholds as classification labels (normal vs. obese BMI). Ensemble classification accuracies of 72.8 ± 1.2% and 73.9 ± 2.3% were obtained for ResNet152 and DenseNet201 networks that used transfer learning, with 5.4–12.3% and 6.1–13.0% improvements relative to classifications based on single shape metrics, respectively. Transfer learning always outperformed direct learning and conventional linear support vector machines with 3.4–8.7% and 4.9–6.0% improvements in ensemble classification accuracies, respectively. Our proposed OMTNet method may offer a powerful transfer learning framework that can be extended to other vertex-wise brain structural/functional imaging measures.

Designing high-performance electromagnetic functional materials and artificial structures is of great significance for electromagnetic wave modulation applications. Optimizing performance in the high-dimensional parameter space of electromagnetic functional materials has posed a challenging problem. This paper proposes the use of reinforcement and transfer learning methods to facilitate the quick and accurate design of wideband circuit analog absorbers (CAA). A trained reinforcement learning network is utilized to comprehend the relationship between reflectivity performance and changes in impedance parameter. Furthermore, the transfer learning method is applied to accelerate the training process, leading in a 20-fold reduction in the number of simulations. To validate the effectiveness of this approach, a double-layer absorber aiming for reflectivity less than −20 dB at 3–10 GHz is designed, requiring only 50 simulations. This demonstrates that the proposed method provides a flexible strategy for the interactive design of equivalent circuit and electromagnetic structural parameters. Moreover, it presents a novel and efficient solution for microwave absorber design, with potential applications across various microwave design fields.

The leather industry plays a crucial role in the production of high-quality leather products to remain competitive in the market. Due to various reasons such as material handling and turning, leather defects can appear at different stages of the production process. This paper proposed deep learning techniques, specifically transfer learning, deep convolutional neural networks (D-CNN), and ensemble learning for automating leather defect detection and classification. The dataset was taken from the open source Kaggle. The dataset was preprocessed from the beginning. However, we performed Error Level Analysis (ELA) to detect possible changes or fraud in the image. We also used data augmentation techniques (resizing, rescaling, flipping, rotation, zooming, and contrasting) on the dataset to improve the model performance. This study investigated deep learning architectures for automatic leather defect detection, comparing them with seven deep convolutional neural networks (D-CNN) models and transfer learning architectures. Furthermore, we introduce and evaluate two novel ensemble models: MER built on transfer learning architecture (MobileNetV2, EfficientNetB0, and ResNet50) and AZL built on D-CNN architecture (AlexNet, ZfNet, and LeNet). A key contribution of this study is the demonstration of significant performance improvements achieved by strategically integrating squeeze and excitation and label smoothing techniques into transfer learning, D-CNN, and ensemble frameworks. Our proposed MER ensemble method achieves a state-of-the-art of 96.39% for leather defect detection. The proposed model was evaluated using performance metrics including precision, recall, F1-score, AUC, ROC, and Matthews Correlation Coefficient (MCC). Additionally, Grad-CAM was employed for explainable AI to visualize and interpret the model’s decision-making process. Comparative analysis against separate transfer learning and D-CNN models fails to establish the superior performance of our MER ensemble, which highlights the potential to significantly advance automated quality inspection in leather production. This study provides insights into evaluating the best deep learning techniques for leather defect classification, paving the way for more accurate, efficient, and reliable industrial applications. Proposes a robust deep learning-based framework for automated leather defect detection using transfer learning, deep CNNs, and two novel ensemble models (MER and AZL), enhanced with squeeze-and-excitation and label smoothing techniques. Achieves state-of-the-art performance with the proposed MER ensemble model, achieve 96.39% accuracy, and demonstrates superior results across precision, recall, F1-score, AUC–ROC, and MCC. Integrates Error Level Analysis (ELA), extensive data augmentation, and Grad-CAM–based explainable AI to ensure data reliability, improve generalization, and provide transparent visual interpretation of defect regions for industrial quality inspection.