Machine learning (ML) has emerged as a transformative force, revolutionizing various industries and domains including computer vision, autonomous driving, and robotics. As ML applications become more widespread, the need for robustness and reliability in machine learning models has become increasingly critical. However, the deployment of ML models in practical scenarios raises significant challenges related to robustness, reliability, and adaptability. Challenges such as susceptibility to corrupted data, issues related to generalization across diverse datasets, and the interpretability of complex models represent significant hurdles in the deployment of ML solutions in real-world scenarios.
This Research Topic seeks to address these challenges by exploring and showcasing the latest advancements in robust machine learning methodologies. By fostering collaboration among researchers, practitioners, and industry experts, we aim to contribute to the development of resilient ML models capable of withstanding adversarial environments and performing reliably across various contexts.
The primary goal of this themed article collection is to provide a comprehensive platform for researchers, practitioners, and industry professionals to share novel insights, methodologies, and applications that enhance the robustness of machine learning models. By achieving these goals, this collection aims to contribute significantly to the maturation of robust machine learning, creating a lasting impact on the reliability, security, and ethical considerations surrounding the deployment of machine learning models in diverse and dynamic environments.
Topics of interest include, but are not limited to:
Label-ambiguity learning: Developing new methods for multi-label learning, partial-label learning, etc.
Generalization and transfer learning: Developing new methods to improve model generalization across diverse datasets and domains including transfer learning, incremental learning, meta-learning, few-shot learning, etc.
Model robustness in open-world: Techniques to enhance model resilience in the open world, including open-set detection, out-of-distribution detection, adversarial attacks, anomaly detection, etc.
Data quality and preprocessing: Strategies for handling diverse data to improve model robustness, including noisy data, time series data, etc.
Data efficient utilization: Developing effective training methods to improve the utilization of data including clustering, etc.
Model interpretability: Developing new deep learning methods with better explainability including causal inference, decoupling representation learning, etc.
Information for authors:
We welcome original research articles, reviews, and case studies that significantly contribute to the understanding and advancement of robust machine learning. Submissions should offer practical insights, demonstrate the effectiveness of proposed techniques, and address the challenges associated with deploying robust machine learning models in real-world scenarios.
Keywords:
Machine Learning, Meta Learning, Robust Representation, Data-Efficient Learning, Open-World
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Machine learning (ML) has emerged as a transformative force, revolutionizing various industries and domains including computer vision, autonomous driving, and robotics. As ML applications become more widespread, the need for robustness and reliability in machine learning models has become increasingly critical. However, the deployment of ML models in practical scenarios raises significant challenges related to robustness, reliability, and adaptability. Challenges such as susceptibility to corrupted data, issues related to generalization across diverse datasets, and the interpretability of complex models represent significant hurdles in the deployment of ML solutions in real-world scenarios.
This Research Topic seeks to address these challenges by exploring and showcasing the latest advancements in robust machine learning methodologies. By fostering collaboration among researchers, practitioners, and industry experts, we aim to contribute to the development of resilient ML models capable of withstanding adversarial environments and performing reliably across various contexts.
The primary goal of this themed article collection is to provide a comprehensive platform for researchers, practitioners, and industry professionals to share novel insights, methodologies, and applications that enhance the robustness of machine learning models. By achieving these goals, this collection aims to contribute significantly to the maturation of robust machine learning, creating a lasting impact on the reliability, security, and ethical considerations surrounding the deployment of machine learning models in diverse and dynamic environments.
Topics of interest include, but are not limited to:
Label-ambiguity learning: Developing new methods for multi-label learning, partial-label learning, etc.
Generalization and transfer learning: Developing new methods to improve model generalization across diverse datasets and domains including transfer learning, incremental learning, meta-learning, few-shot learning, etc.
Model robustness in open-world: Techniques to enhance model resilience in the open world, including open-set detection, out-of-distribution detection, adversarial attacks, anomaly detection, etc.
Data quality and preprocessing: Strategies for handling diverse data to improve model robustness, including noisy data, time series data, etc.
Data efficient utilization: Developing effective training methods to improve the utilization of data including clustering, etc.
Model interpretability: Developing new deep learning methods with better explainability including causal inference, decoupling representation learning, etc.
Information for authors:
We welcome original research articles, reviews, and case studies that significantly contribute to the understanding and advancement of robust machine learning. Submissions should offer practical insights, demonstrate the effectiveness of proposed techniques, and address the challenges associated with deploying robust machine learning models in real-world scenarios.
Keywords:
Machine Learning, Meta Learning, Robust Representation, Data-Efficient Learning, Open-World
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.