用知识蒸馏解决生物医学神经场持续学习中的遗忘问题。
Knowledge Distillation for Continual Learning of Biomedical Neural Fields
- 通过知识蒸馏保留旧知识，缓解神经场在增量学习中的灾难性遗忘。
- 在心脏动态MRI数据上验证，模型规模增大时遗忘更严重，蒸馏可有效抑制。
- 适用于需要长期更新的医学图像建模场景，如实时影像分析。
神经场作为轻量、连续且可微的信号表示，在（生物）医学成像中日益广泛应用。然而，与体素网格等离散表示不同，神经场难以直接扩展。由于本质上是神经网络，当模型面对新数据时，先前以神经场表示的信号会因灾难性遗忘而退化。本文研究了不同神经场方法在持续学习中遗忘程度的差异，并提出一种缓解策略。实验设定为数据逐步到达，仅可用最新数据拟合神经场。在心脏动态磁共振影像数据集上，我们证明当时空域扩大或信号维度增加时，知识蒸馏能有效减轻灾难性遗忘。结果表明，遗忘程度在很大程度上取决于所采用的神经场模型，而蒸馏可实现神经场的持续学习。
原文摘要 · Abstract (English)
Neural fields are increasingly used as a light-weight, continuous, and differentiable signal representation in (bio)medical imaging. However, unlike discrete signal representations such as voxel grids, neural fields cannot be easily extended. As neural fields are, in essence, neural networks, prior signals represented in a neural field will degrade when the model is presented with new data due to catastrophic forgetting. This work examines the extent to which different neural field approaches suffer from catastrophic forgetting and proposes a strategy to mitigate this issue. We consider the scenario in which data becomes available incrementally, with only the most recent data available for neural field fitting. In a series of experiments on cardiac cine MRI data, we demonstrate how knowledge distillation mitigates catastrophic forgetting when the spatiotemporal domain is enlarged or the dimensionality of the represented signal is increased. We find that the amount of catastrophic forgetting depends, to a large extent, on the neural fields model used, and that distillation could enable continual learning in neural fields.
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