Scalable Robust Models Under Adversarial Data Corruption
The presence of noise and corruption in real-world data can be inevitably caused by accidental outliers, transmission loss, or even adversarial data attacks. Unlike traditional random noise usually assume a specific distribution with low corruption ratio, the data collected from crowdsourcing or lab...
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Virginia Tech
2019
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| Online Access: | http://hdl.handle.net/10919/88833 |