“Explainability in Computer Vision: A Machine Learning Engineer’s Overview,” a Presentation from AltaML

Navaneeth Kamballur Kottayil, Lead Machine Learning Developer at AltaML, presents the “Explainability in Computer Vision: A Machine Learning Engineer’s Overview” tutorial at the May 2021 Embedded Vision Summit.

With the increasing use of deep neural networks in computer vision applications, it has become more difficult for developers to explain how their algorithms work. This can make it difficult to establish trust and confidence among customers and other stakeholders, such as regulators. Lack of explainability also makes it more difficult for developers to improve their solutions.

In this talk, Kottayil introduces methods for enabling explainability in deep-learning-based computer vision solutions. He also illustrates some of these techniques via real-world examples, and shows how they can be used to improve customer trust in computer vision models, to debug computer vision models, to obtain additional insights about data and to detect bias in models.

See here for a PDF of the slides.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



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