Low Quality Sample

Low-quality samples pose a significant challenge across various machine learning applications, hindering model performance and reliability. Current research focuses on developing methods to either mitigate the negative impact of these samples during training (e.g., by weighting them differently or focusing on recognizable features) or to pre-screen samples for quality before processing. This involves adapting existing architectures like GANs and employing techniques such as uncertainty learning and quality-aware functions. Addressing this challenge is crucial for improving the robustness and accuracy of machine learning models in real-world scenarios where imperfect data is common.