Task Prediction

Task prediction focuses on accurately identifying the specific task a model should perform, particularly crucial in continual learning scenarios where models encounter sequential tasks without explicit task labels at inference time. Current research emphasizes developing robust task predictors using confidence scores from probability distributions, leveraging semantic information from input data (e.g., image tokens), or employing gradient-based methods. These advancements are vital for improving the efficiency and adaptability of machine learning models in diverse applications, such as medical image analysis and drug discovery, where continuous adaptation to new data and tasks is essential.