Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging

By Yue Deng, KaWai Chen, Yilin Shen, Hongxia Jin. 2019

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.

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1. Xiangyu Zeng
2. Yilin Shen
3. Hongxia Jin
4. Zixuan Ke
5. Fei Mi
6. Rui Huang
7. Qi Zhu

Citations in the corpus

• A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, background): “…train a slot filling model independently (Yao et al., 2014; Peng et al., 2015; Kurata et al., 2016; Hakkani-T¨ur et al., 2016; Liu and Lane, 2016; Deng et al., 2019; Ray et al., 2019) or jointly with the other intent detection task in SLU (Guo et al., 2014; Liu and Lane, 2016; Zhang and Wang,…”