Target Supervision

Target supervision in machine learning focuses on improving model training by crafting informative training signals, often beyond simple one-hot encodings. Current research emphasizes developing robust strategies to handle noisy or incomplete target information, particularly in scenarios like domain adaptation and self-supervised learning, often employing techniques like soft labels, prototype-based methods, and consistency-based regularization. These advancements aim to enhance model generalization and robustness, particularly in challenging real-world applications where perfectly labeled data is scarce or unavailable, leading to improved performance in tasks such as object recognition, audio-visual separation, and crowd localization.