T-CIL：利用对抗扰动进行类增量学习中的温度缩放校准
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning
AI总结：
针对类增量学习中的置信度校准问题，提出T-CIL方法，利用对抗扰动示例调整温度缩放，无需旧任务验证集，显著提升校准性能且对准确性影响小。
摘要：
我们研究了类增量学习中的模型置信度校准问题，其中模型从具有不同类别集合的连续任务中学习。虽然现有工作主要关注准确性，但保持校准置信度在很大程度上被忽视。不幸的是，大多数事后校准技术并不适用于类增量学习中典型的旧任务数据有限记忆的情况，因为保留足够的验证集是不切实际的。因此，我们提出了T-CIL，一种新颖的温度缩放方法，用于类增量学习，无需旧任务的验证集，利用来自记忆的对抗扰动示例。直接使用示例不足以进行温度优化，因为它们已用于训练。T-CIL的关键思想是通过基于特征距离调整扰动方向，对旧任务的示例进行比新任务更强的扰动，并使用新任务验证集确定单一幅度。这一策略使得从新任务计算的扰动幅度也适用于旧任务，利用了旧任务准确性低于新任务的趋势。我们通过实验证明，T-CIL在真实数据集上的校准方面显著优于各种基线，并且可以与现有类增量学习技术集成，对准确性影响最小。
英文摘要：
We study model confidence calibration in class-incremental learning, where models learn from sequential tasks with different class sets. While existing works primarily focus on accuracy, maintaining calibrated confidence has been largely overlooked. Unfortunately, most post-hoc calibration techniques are not designed to work with the limited memories of old-task data typical in class-incremental learning, as retaining a sufficient validation set would be impractical. Thus, we propose T-CIL, a novel temperature scaling approach for class-incremental learning without a validation set for old tasks, that leverages adversarially perturbed exemplars from memory. Directly using exemplars is inadequate for temperature optimization, since they are already used for training. The key idea of T-CIL is to perturb exemplars more strongly for old tasks than for the new task by adjusting the perturbation direction based on feature distance, with the single magnitude determined using the new-task validation set. This strategy makes the perturbation magnitude computed from the new task also applicable to old tasks, leveraging the tendency that the accuracy of old tasks is lower than that of the new task. We empirically show that T-CIL significantly outperforms various baselines in terms of calibration on real datasets and can be integrated with existing class-incremental learning techniques with minimal impact on accuracy.
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