Ambiguous Input

Ambiguous input, encompassing haphazard data streams, out-of-distribution samples, and inherently unclear inputs, poses a significant challenge to the reliability and robustness of machine learning models. Current research focuses on developing methods to detect and handle such ambiguity, employing techniques like self-attention mechanisms, generative models, and metric learning integrated with various deep learning architectures (e.g., transformers, autoencoders). Addressing ambiguous input is crucial for improving the trustworthiness and generalizability of AI systems across diverse applications, particularly in safety-critical domains like healthcare and autonomous systems.