AI, Machine Learning and Computer Vision Meetup
Aug 15, 2024 at 2 PM London / 6:30 PM India
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SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image Translation while Maintaining Stereo Constraints
Vasudha Venkatesan
University of Freiburg/ex-German Aerospace Center
In this talk we’ll explore SyntStereo2Real, a lightweight I2I translation framework for stereo pairs considering a semantic-consistent translation of left and right images. Utilization of edge maps and an efficient warping loss are used to improve the matching of the generated pairs. This approach outperformed the current SOTA on remote sensing and autonomous driving datasets!
About the Speaker
Vasudha Venkatesan is a researcher and engineer with expertise in computer vision and machine learning. She has made significant contributions to the field of remote sensing, particularly addressing challenges related to stereo-matched data and domain generalization.
Elevating Security with Data-Centric AI: A Comprehensive Approach to Surveillance
Dan Gural
Voxel51
Effective machine learning models crucially depend on high-quality data inputs. The security sector, grappling with vast volumes of video data, faces the critical task of building reliable models amidst unique challenges. In this session, we dive into the how by ML engineers are leveling up by prioritizing data-centric workflows to enhance model efficacy. Join us as we unveil strategies to identify errors, analyze embeddings, and assess security models with unparalleled precision using FiftyOne.
About the Speaker
Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.
How to Develop Data Science Projects with Open Source
Jay Cui
Machine Learning Engineer
As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML models…while making it easy to swap in other ML models.
About the Speaker
Jay Cui had worked at BENlabs as a machine learning platform engineer. Prior to that, he was an undergraduate student studying applied mathematics with a minor in computer science at BYU.
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