判别式
生成语法
人工智能
计算机科学
生成模型
自编码
模式识别(心理学)
接头(建筑物)
特征学习
代表(政治)
背景(考古学)
机器学习
语音识别
自然语言处理
深度学习
工程类
古生物学
政治
生物
建筑工程
法学
政治学
作者
Xiaomin Zeng,Yan Song,Zhu Zhuo,Yu Zhou,Yuhong Li,Hui Xue,Lirong Dai,Ian McLoughlin
标识
DOI:10.1109/icassp49357.2023.10095568
摘要
In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD). GeCo exploits a Predictive AutoEncoder (PAE) equipped with self-attention as a generative model to perform frame-level prediction. The output of the PAE together with original normal samples, are used for supervised contrastive representative learning in a multi-task framework. Besides cross-entropy loss between classes, contrastive loss is used to separate PAE output and original samples within each class. GeCo aims to better capture context information among frames, thanks to the self-attention mechanism for PAE model. Furthermore, GeCo combines generative and contrastive learning from which we aim to yield more effective and informative representations, compared to existing methods. Extensive experiments have been conducted on the DCASE2020 Task2 development dataset, showing that GeCo outperforms state-of-the-art generative and discriminative methods.
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