Current fake audio detection algorithms have achieved promising performances
on most datasets. However, their performance may be significantly degraded when
dealing with audio of a different dataset. The orthogonal weight modification
to overcome catastrophic forgetting does not consider the similarity of genuine
audio across different datasets. To overcome this limitation, we propose a
continual learning algorithm for fake audio detection to overcome catastrophic
forgetting, called Regularized Adaptive Weight Modification (RAWM). When
fine-tuning a detection network, our approach adaptively computes the direction
of weight modification according to the ratio of genuine utterances and fake
utterances. The adaptive modification direction ensures the network can
effectively detect fake audio on the new dataset while preserving its knowledge
of old model, thus mitigating catastrophic forgetting. In addition, genuine
audio collected from quite different acoustic conditions may skew their feature
distribution, so we introduce a regularization constraint to force the network
to remember the old distribution in this regard. Our method can easily be
generalized to related fields, like speech emotion recognition. We also
evaluate our approach across multiple datasets and obtain a significant
performance improvement on cross-dataset experiments.

我们提出了一种连续学习算法用于检测伪造音频以克服灾难性遗忘，并引入正则化约束来控制特征分布，通过自适应权重修改方向，网络可以在新数据集上有效检测伪造音频，同时保留对旧模型的知识，实现降低灾难性遗忘。我们在多个数据集上评估了我们的方法，并在跨数据集实验中取得了显著的性能改进。

克服虛假音頻檢測中的災難性遺忘

Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio  Detection

Standard gradient descent algorithms applied to sequences of tasks are known
to produce catastrophic forgetting in deep neural networks. When trained on a
new task in a sequence, the model updates its parameters on the current task,
forgetting past knowledge.
This article explores scenarios where we scale the number of tasks in a
finite environment. Those scenarios are composed of a long sequence of tasks
with reoccurring data.
We show that in such setting, stochastic gradient descent can learn,
progress, and converge to a solution that according to existing literature
needs a continual learning algorithm. In other words, we show that the model
performs knowledge retention and accumulation without specific memorization
mechanisms.
We propose a new experimentation framework, SCoLe (Scaling Continual
Learning), to study the knowledge retention and accumulation of algorithms in
potentially infinite sequences of tasks. To explore this setting, we performed
a large number of experiments on sequences of 1,000 tasks to better understand
this new family of settings.
We also propose a slight modifications to the vanilla stochastic gradient
descent to facilitate continual learning in this setting.
The SCoLe framework represents a good simulation of practical training
environments with reoccurring situations and allows the study of convergence
behavior in long sequences. Our experiments show that previous results on short
scenarios cannot always be extrapolated to longer scenarios.

本文探讨了在有限环境中增长任务数量的情景下，通过一种新的实验框架 SCoLe，借助随机梯度下降法，实现在长序列的任务中进行知识积累和保留，提出了一种改进的随机梯度下降方法以便于在此设置中进行继续学习的算法，并通过合适的实验框架展示了新的可持续性学习学习机制。