Moderation Team
Constructing high-quality datasets that are diverse, realistic, and extensive enough to train autonomous and ADAS sensors is crucial for ensuring reliable performance in complex and unpredictable driving scenarios where lives are at risk.
Today, developing deep-learning models for these safety systems and sensor models requires levels of accuracy that are virtually impossible to achieve using current development paradigms.
In this informative webinar, experts from Tensorleap, Cognata, and Foresight will discuss ways to overcome the complexities of developing automotive AI, resolving model weaknesses, and pinpointing the data you need.
Key topics and takeaways:
* Learn about the challenges of generating synthetic data and how to create balanced, high-quality datasets with data-driven insights
* Discover how to pinpoint and resolve edge cases before failures in production
* Find out how to use innovative applied explainability techniques to overcome critical dataset construction issues and pinpoint and bridge domain gaps in minimal time
* Understand how to optimize your sensor suite virtualization and simulation strategies to meet your organization’s needs
Yotam Azriel, CTO & Co-founder, Tensorleap
Danny Atsmon, CEO & Founder, Cognata
Ivgeny Kopilevich, Head of Algorithms, Foresight
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