Few-shot learning (FSL) aims to classify a novel object into a specific category under limited training samples. This is a challenging task since (1) the features expressed by pre-trained knowledge introduce perceived...
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Few-shot learning (FSL) aims to classify a novel object into a specific category under limited training samples. This is a challenging task since (1) the features expressed by pre-trained knowledge introduce perceived bias and then constrain the classification space, and (2) the use of general hallucination techniques based on global features fails to escape the limited classification space, resulting in suboptimal improvements. To solve these issues, this paper proposes an interventional feature generation (IFG) method. Specifically, we first use the relations of the categories or instances as interventional operations to implicitly constrain the feature representations (pre-trained knowledge) into different classification subsets. Then, we employ a parameter-free feature generation strategy to enrich each subset’s training samples of the support category. In other words, IFG provides a multi-subsets learning strategy to reduce the influence of perceived bias, enrich the diversity of generated features, and improve the robustness of the few-shot classifier. We apply our method to four benchmark datasets and observe state-of-the-art performance across all experiments. Specifically, compared to the baseline on the Mini-ImageNet dataset, our approach yields accuracy improvements of 6.03% and 3.46% for 1 and 5 support training samples, respectively. Furthermore, the proposed interventional feature generation technique can improve classifier performance in other FSL methods, demonstrating its versatility and potential for broader applications. The code is available at https://***/ShuoWangCS/IFG-FSL/.
The book highlights innovative ideas, cutting-edge findings, and novel techniques, methods and applications touching on all aspects of technology and intelligence in smart city management and services. Above all, it e...
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ISBN:
(数字)9783319987767
ISBN:
(纸本)9783319987750
The book highlights innovative ideas, cutting-edge findings, and novel techniques, methods and applications touching on all aspects of technology and intelligence in smart city management and services. Above all, it explores developments and applications that are of practical use and value for cyber Intelligence-related methods, which are frequently used in the context of city management and services.
This book provides a comprehensive exploration of how Artificial Intelligence (AI) is being applied in the fields of cybersecurity and digital forensics. The book delves into the cutting-edge techniques that are resh...
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ISBN:
(数字)9783031893278
ISBN:
(纸本)9783031893261;9783031893292
This book provides a comprehensive exploration of how Artificial Intelligence (AI) is being applied in the fields of cybersecurity and digital forensics. The book delves into the cutting-edge techniques that are reshaping the way we protect and investigate digital information. From identifying cyber threats in real-time to uncovering hidden evidence in complex digital cases, this book offers practical insights and real-world examples. Whether you’re a professional in the field or simply interested in understanding how AI is revolutionizing digital security, this book will guide you through the latest advancements and their implications for the future.
Includes application of AI in solving real cybersecurity and digital forensics challenges, offering tangible examples;
Shows how AI methods from machine / deep learning to NLP can be used for cyber defenses and in forensic investigations;
Explores emerging trends and future possibilities, helping readers stay ahead of the curve in a rapidly evolving field.
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