VisDA: Teach AIs to See From Simulation to Real Photos
The VisDA challenge show how machines can learn in one world and work in another.
Researchers train models on simple, fake images and then test them on real photos to tackle the tricky problem called simulation-to-reality shift.
This project builds a large-scale set of pictures for two jobs: teaching computers to recognize whole objects and to trace them inside a picture — that are image classification and image segmentation.
It includes hundreds of thousands of images, so the tests is hard and real.
The idea is to see if a model trained on clean, made-up scenes can still do well when shown messy, real life shots.
People use this to make more useful tools for robots, self-driving cars and photo apps.
The team also run simple tests to show how different methods perform.
You can think of VisDA as a bridge, helping lab work step out into the world, and it keeps pushing for better ways to teach machines to look, learn, and adapt.
Read article comprehensive review in Paperium.net:
VisDA: The Visual Domain Adaptation Challenge
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