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GTC ON-DEMAND

AI & Deep Learning Research
Presentation
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Investigating Data Augmentation Strategies for Advancing Deep Learning Training
Abstract:
We saw the huge success of the deep learning paradigm and the superhuman capability in numerous benchmarks in image, video, audio, or text. However, it poses huge challenges as adopting the methods in industrial applications (mainly due to the lack of quality tracking data) as the neural networks consume enormous parameters and require relatively huge quality training data. We'll aim for investigating the "data augmentation" strategies increasing quality training data for robust inference across different learning problems mainly in image, video, 3D, and IoT data streams. We'll first quantify the importance of training data for deep neural networks then review numerous strategies, such as crawling from the web, utilizing generative models, 3D computer graphics, augmented reality, engagement in social media, gaming, etc. We'll compare the effectiveness among the diverse strategies. As generally taking the data from other domains, we also need to deal with the cross-domain learning problem. We'll provide detailed insights from our recent work published in top conferences (e.g., CVPR, ICCV, AAAI, etc.) and those cases in industrial applications.
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8391
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