Generative Adversarial Networks (GANs) are capable of effectively synthesising new realistic images and estimating the potential distribution of samples utilising adversarial learning. Nevertheless, conventional GANs require a large amount of training data samples to produce plausible results. Inspired by the capacity for humans to quickly learn new concepts from a small number of examples, several meta-learning approaches for the few-shot datasets are presented. However, most of meta-learning algorithms are designed to tackle few-shot classification and reinforcement learning tasks. Moreover, the existing meta-learning models for image generation are complex, thereby affecting the length of training time required. Fast Adaptive Meta-Learning (FAML) based on GAN and the encoder network is proposed in this study for few-shot image generation. This model demonstrates the capability to generate new realistic images from previously unseen target classes with only a small number of examples required. With 10 times faster convergence, FAML requires only one-fourth of the trainable parameters in comparison baseline models by training a simpler network with conditional feature vectors from the encoder, while increasing the number of generator iterations. The visualisation results are demonstrated in the paper. This model is able to improve few-shot image generation with the lowest FID score, highest IS, and comparable LPIPS to MNIST, Omniglot, VGG-Faces, and mini ImageNet datasets. The source code is available on https://github.com/phaphuang/FAML .

Railway track defects pose significant safety risks and can lead to accidents, economic losses, and loss of life. Traditional manual inspection methods are either time-consuming, costly, or prone to human error. This paper proposes RailTrack-DaViT, a novel vision transformer-based approach for railway track defect classification. By leveraging the Dual Attention Vision Transformer (DaViT) architecture, RailTrack-DaViT effectively captures both global and local information, enabling accurate defect detection. The model is trained and evaluated on multiple datasets including rail, fastener and fishplate, multi-faults, and ThaiRailTrack. A comprehensive analysis of the model’s performance is provided including confusion matrices, training visualizations, and classification metrics. RailTrack-DaViT demonstrates superior performance compared to state-of-the-art CNN-based methods, achieving the highest accuracies: 96.9% on the rail dataset, 98.9% on the fastener and fishplate dataset, and 98.8% on the multi-faults dataset. Moreover, RailTrack-DaViT outperforms baselines on the ThaiRailTrack dataset with 99.2% accuracy, quickly adapts to unseen images, and shows better model stability during fine-tuning. This capability can significantly reduce time consumption when applying the model to novel datasets in practical applications.

As the next frontier in wireless communication, the landscape of 6G technologies is characterized by its rapid evolution and increasing complexity, driven by the need to address global challenges such as ubiquitous connectivity, ultra-high data rates, and intelligent applications. Given the significance of 6G in shaping the future of communication and its potential to revolutionize various industries, understanding the technological evolution within this domain is crucial. Traditional topic modeling approaches fall short in adapting to the rapidly changing and highly complex nature of patent-based topic analysis in this field, thereby impeding a comprehensive understanding of the advanced technological evolution in terms of capturing temporal changes and uncovering semantic relationships. This study delves into the exploration of the evolving technologies of 6G in patent data through a novel dynamic topic modeling framework. Specifically, this work harnesses the power of large language models to effectively reduce the noise in patent data pre-processing using a prompt-based summarization technique. Then, we propose an enhanced dynamic topic modeling framework based on BERTopic to capture the time-aware features of evolving topics across periods. Additionally, we conduct comparative analysis in contextual embedding techniques and leverage SBERT pre-trained on patent data to extract the content semantics in domain-specific patent data within this framework. Finally, we apply the weak signal analysis method to identify the emerging topics in 6G technology over the periods, which makes the topic evolution analysis more interpretable than traditional topic modeling methods. The empirical results, which were validated by human experts, show that the proposed method can effectively uncover patterns of technological evolution, thus enabling its potential application to enhance strategic decision-making and stay ahead in the highly competitive and rapidly evolving technological sector.