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Taskonomy: Disentangling Transfer Learning for Perception (CVPR 2018 Best Paper Winner) with Amir Zamir - TWiML Talk #164
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Taskonomy: Disentangling Transfer Learning for Perception (CVPR 2018 Best Paper Winner) with Amir Zamir - TWiML Talk #164
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
ratings:
Length:
48 minutes
Released:
Jul 16, 2018
Format:
Podcast episode
Description
In this episode I'm joined by Amir Zamir, Postdoctoral researcher at both Stanford & UC Berkeley. Amir joins us fresh off of winning the 2018 CVPR Best Paper Award for co-authoring "Taskonomy: Disentangling Task Transfer Learning." In this work, Amir and his coauthors explore the relationships between different types of visual tasks and use this structure to better understand the types of transfer learning that will be most effective for each, resulting in what they call a “computational taxonomic map for task transfer learning.” In our conversation, we discuss the nature and consequences of the relationships that Amir and his team discovered, and how they can be used to build more effective visual systems with machine learning. Along the way Amir provides a ton of great examples and explains the various tools his team has created to illustrate these concepts.
Released:
Jul 16, 2018
Format:
Podcast episode
Titles in the series (100)
This Week in ML & AI – 8/12/16: Another huge machine learning acquisition + AI in the Olympics: This Week in Machine Learning & AI brings you the… by The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)