Semantic Loss

Semantic loss in machine learning focuses on improving model performance by incorporating semantic information – the meaning and relationships between data elements – into the training process. Current research emphasizes developing loss functions that explicitly consider semantic relationships, often within multimodal contexts (e.g., image-text, audio-video), and employing transformer architectures for improved representation learning. This approach is significant because it addresses limitations of traditional loss functions that ignore semantic nuances, leading to more robust and accurate models with applications across diverse fields like natural language processing, computer vision, and communication systems.