MT-CC: Multi-Group Temperature Scaling for Asymmetric Calibration Behavior in Class-Incremental Learning

Key points

• As deep neural networks are required to continuously adapt to evolving data, class-incremental learning (CIL) has become an active research area.1
• Although temperature scaling (TS) is effective for static calibration, it has rarely been explored in continual settings where data distributions evolve over time.1
• Our key contribution is to design multiple temperature parameters through a lightweight grouping network that separates samples by confidence statistics and task-aware signals in CIL.1
• Experiments demonstrate that MT-CC significantly improves calibration performance while maintaining accuracy, offering a principled framework for reliable continual learning.1

Sources (1)

• [1]MT-CC: Multi-Group Temperature Scaling for Asymmetric Calibration Behavior in Class-Incremental Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 02:37 AM

  In this paper, we introduce Multi-Group Temperature Scaling for Continual Calibration (MT-CC), a novel post-hoc calibration framework that addresses the asymmetric calibration behavior in CIL, where the rate of confidence changes does not align with the rate of accuracy degradation across tasks, leading to different levels of miscalibration.

  As deep neural networks are required to continuously adapt to evolving data, class-incremental learning (CIL) has become an active research area.

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