Model-based deep learning: Applications improving imaging and communications
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This AI for Good Webinar introduces various approaches to model-based deep learning. Discover examples of such model-based deep networks applied to image deblurring, image separation, super resolution in ultrasound and microscopy, efficient communication systems, and finally see how model-based methods can also be used for efficient diagnosis of COVID-19 using X-ray and ultrasound.
Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, meaning a lack of interpretability, and the need for very large training sets. On the other hand, signal processing and communications have traditionally relied on classical statistical modeling techniques that utilize mathematical formulations representing the underlying physics, prior information and additional domain knowledge.
Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. Learn more about model-based deep learning which merge parametric models with optimization tools and classical algorithms leading to efficient, interpretable networks from reasonably sized training sets.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.