Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging

Yue Deng; KaWai Chen; Yilin Shen; Hongxia Jin

ICASSP 2019accepted0 citations

Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging

Yue Deng, KaWai Chen, Yilin Shen, Hongxia Jin

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.

BibTeX

@inproceedings{icassp2019_adversarialmulti,
  title = {Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging},
  author = {Yue Deng and KaWai Chen and Yilin Shen and Hongxia Jin},
  booktitle = {ICASSP 2019},
  year = {2019}
}