Continual Calibration

Continual calibration addresses the challenge of maintaining the accuracy of machine learning models in dynamic environments where data distributions shift over time. Research focuses on developing efficient algorithms, often involving data compression and alternative optimization methods like bit-flipping networks, to adapt quantized models to new data streams without requiring full retraining. This is crucial for deploying models on resource-constrained edge devices and improving the reliability of predictions in continual learning scenarios, impacting fields like robotics and real-time data processing. The development of robust benchmarks and improved calibration strategies for various model architectures, including diffusion models, is also a key area of investigation.