@inproceedings{1d3502db2d8c41ffb1d6184d66784677,
title = "Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging",
abstract = "We introduce an adversarial multi-label classification (ADMLC) framework to improve the robustness and performance of existing algorithms on multi-domain signals. The core contribution of our ADMLC is the innovation of an 'adversarial module' that serves as a critic to provide augmenting information to improve supervised learning in multi label classification (MLC) tasks. Our approach is not intended to be regarded as an emerging competitor for many well-established algorithms in the field. In fact, many existing deep and shallow architectures can all be adopted as building blocks integrated in the ADMLC framework. We show the performance and generalization ability of ADMLC on diverse tasks including audio and image tagging.",
keywords = "adversarial learning, audio tagging, multi label prediction",
author = "Yue Deng and Kawai Chen and Yilin Shen and Hongxia Jin",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8683651",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3252--3256",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "美国",
}