用回溯翻译器防止持续学习中旧数据性能下降
Traceback Translators Against Forgetting in Continual Fake Speech Detection
- 在冻结检测器中引入域翻译网络，将新特征映射回原始空间
- 相比传统重训练，检测率更高且计算成本更低
- 适合需要持续更新的语音伪造检测系统
语音伪造检测面临生成模型不断进步的挑战。尽管持续学习被广泛认为是更新模型的有效策略，但其常导致对先前数据的性能下降（灾难性遗忘）。本文提出一种抗遗忘方案：在冻结的检测器中引入回溯翻译器网络，通过域翻译将新特征空间映射回原始空间。实验表明，该方法在保持旧数据检测精度的同时，显著提升对新样本的检测率，且计算开销远低于传统重训练。
原文摘要 · Abstract (English)
Fake speech detectors are increasingly challenged by the development of new and more accurate generative models. To cope with this problem, continual learning techniques are nowadays widely considered feasible strategies for updating models to new datasets, but they also lead to decreased performance on previously seen samples (catastrophic forgetting). In this work, we propose a forgetting-resilient solution based on the adoption of domain translators within a frozen detector, which remaps the new feature spaces into the original ones by means of a traceback translator network. Experimental results show that this strategy enables the achievement of high detection rates with respect to traditional retraining, while minimizing the computational effort and preserving the detection accuracy on previous data.
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