The field of machine learning is moving towards developing more robust and reliable models, with a focus on adversarial robustness and anomaly detection. Recent research has introduced novel methods for detecting and mitigating adversarial attacks, such as the use of prediction inconsistency and dynamic epsilon scheduling. Additionally, there have been significant advancements in anomaly detection, including the development of new frameworks for multi-label classification and video anomaly detection. These innovations have the potential to improve the performance and trustworthiness of machine learning models in a variety of applications. Noteworthy papers include Confidential Guardian, which proposes a framework for preventing the misuse of cautious predictions, and INP-Former++, which achieves state-of-the-art performance in single-class, multi-class, and few-shot anomaly detection tasks. Overall, the field is advancing towards more secure and dependable machine learning models.
Advancements in Adversarial Robustness and Anomaly Detection
Sources
MemoryOut: Learning Principal Features via Multimodal Sparse Filtering Network for Semi-supervised Video Anomaly Detection
Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers