True Class

"True class" research broadly focuses on improving the accuracy and efficiency of classification tasks across diverse data types and learning paradigms. Current efforts concentrate on developing novel algorithms and model architectures, such as graph neural networks and physics-informed neural networks, to address challenges like class imbalance, catastrophic forgetting in incremental learning, and the limitations of traditional activation functions. This research is significant for advancing machine learning capabilities in various fields, including medical image analysis, natural language processing, and robotics, by enabling more robust and reliable classification models.