Inter Class Feature

Inter-class feature research focuses on optimizing the separation and relationships between different classes' feature representations in machine learning models. Current efforts concentrate on developing loss functions and data augmentation techniques that improve inter-class distances while mitigating intra-class variance, often leveraging graph neural networks or interpolation methods like mixup. This work aims to enhance model generalization, particularly in few-shot learning and out-of-distribution detection, leading to more robust and efficient algorithms for various applications including image recognition, speech processing, and gait analysis. Improved inter-class feature management is crucial for building more reliable and adaptable AI systems.