Pseudo Target

Pseudo-targets are synthetic training data used to augment limited real-world datasets, improving the performance of machine learning models. Current research focuses on generating high-quality pseudo-targets from various sources, including visually-grounded speech models, existing prediction maps, and teacher models, often employing techniques like clustering and adaptive anchor assignment to mitigate inconsistencies. This approach is proving valuable across diverse applications, such as spoken language understanding, test-time adaptation, and semi-supervised object detection, enhancing model accuracy and efficiency, particularly in scenarios with scarce labeled data.