I had the opportunity to present recent work Leveraging Domain Knowledge For Deep Learning Based Computer Vision at Knowledge Graph Conference 2022. The blurb for this talk is as following:

Deep learning models require massive amounts of data to be performed accurately. As the world is inherently interconnected, we can leverage relationships amongst identifiable objects to improve Deep Learning. For example, a shingle roof can not be a tile roof, but both are roofs. So, we set ourselves a challenge: “How can we leverage the knowledge and the relationship amongst the things we see in our world to improve our data, software systems, and the deep learning model?”.

In this presentation, we share our experiences with knowledge graphs as a technique to model domain knowledge and reason about it to derive an embedding. We have leveraged these embeddings in numerous applications to build a more scalable, reliable, and efficient AI System. The applications include improving the quality and richness of our datasets, identifying gaps in annotators’ knowledge, utilizing existing data to synthesize new objects on the fly, and also increasing the efficiencies of deep learning models.

Slides can be found here. The video recording is located here!