Multi Stage Training

Multi-stage training is a machine learning technique that improves model performance by iteratively training a model or its components in distinct phases, often using different data, loss functions, or architectures at each stage. Current research focuses on applications across diverse fields, including sound event detection (using transformers and CRNNs), natural language processing (employing BERT and UL2 models), and image recognition (leveraging lightweight mobile networks). This approach enhances model accuracy and efficiency, particularly in scenarios with limited data or computational resources, and is proving valuable for improving various applications from speech recognition to fine-grained visual classification.