This article was contributed by Nikita Johnson.
The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically.
However, despite their appeal, such models often fail to distinguish synthetic images from real images, necessitating domain adaptation algorithms to manipulate these models before they can be successfully applied. Dilip Krishnan, Research Scientist at Google, is working on two approaches to the problem of unsupervised visual domain adaptation (both of which outperform current state-of-the-art methods.)
What you can find in the full article:
- Tell us more about your work, and give us a short teaser to your session
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- What are the key factors that have enabled recent advancements in deep learning?
- Which industries do you think deep learning will benefit the most and why?
- What advancements in deep learning would you hope to see in the next 3 years?
To read the original article, click here.
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