63 citations · 68 across the 4 of their papers we have counts for
4 papers
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition
Ting-Yao Hu, Mohammadreza Armandpour, Ashish Shrivastava +3
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthe…
Data Incubation -- Synthesizing Missing Data for Handwriting Recognition
Jen-Hao Rick Chang, Martin Bresler, Youssouf Chherawala +4
In this paper, we demonstrate how a generative model can be used to build a better recognizer through the control of content and style. We are building an online handwriting recogn…
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Ting-Yao Hu, Ashish Shrivastava, Jen-Hao Rick Chang +5
Data augmentation methods usually apply the same augmentation (or a mix of them) to all the training samples. For example, to perturb data with noise, the noise is sampled from a N…
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos +2
While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve prob…