3 accepted papers
Learning a decent representation from unlabeled time series is a challenging task, especially when the time series data is derived from diverse channels at different sampling rates. Our motivation stems from the financial domain, where sparsely labeled covariates are commonly collected at different…
The deep neural networks (DNNs) trained by adversarial training (AT) usually suffered from significant robust generalization gap, i.e., DNNs achieve high training robustness but low test robustness. In this paper, we propose a generic method to boost the robust generalization of AT methods from the…
Non-parallel voice conversion (VC) is a technique of transfer-ring voice from one style to another without using a parallel corpus in model training. Various methods are proposed to approach non-parallel VC using deep neural networks. Among them, CycleGAN-VC and its variants have been widely accepte…