Competition

It is well known that the success of machine learning methods on visual recognition tasks is highly dependent on access to large labeled datasets. Unfortunately, performance often drops significantly when the model is presented with data from a new deployment domain which it did not see in training, a problem known as dataset shift. The VisDA challenge aims to test domain adaptation methods’ ability to transfer source knowledge and adapt it to novel target domains.

For details and instructions on how to participate, please visit the VisDA challenge website, where you can download the datasets and development kits. This challenge includes two tracks:

• Image classification
  
• Image segmentation -- this competition

Participants are welcome to enter in one or both tracks.