Target Representation

Target representation in machine learning focuses on creating effective data encodings that capture essential information for downstream tasks like classification, regression, and tracking. Current research emphasizes improving these representations through various techniques, including joint-embedding predictive architectures, transformer-based models, and masked autoencoders, often incorporating contextual information to enhance robustness and accuracy. These advancements are crucial for improving the performance of numerous machine learning models across diverse applications, from audio and image processing to reinforcement learning and tabular data analysis.