Apple Machine Learning Journal

  • However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly.
  • An alternative to labelling huge amounts of data is to use synthetic images from a simulator.
  • This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images.
  • We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
  • Read the article View the article “Improving the Realism of Synthetic Images”

Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.

@rsalakhu: Very excited for the launch of Apple’s Machine Learning blog: . Check it out!

Vol. 1, Issue 1 ∙ July 2017

Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.

Read the article View the article “Improving the Realism of Synthetic Images”

July 2017

Welcome to the Apple Machine Learning Journal. Here, you can read posts written by Apple engineers about their work using machine learning technologies to help build innovative products for millions of people around the world. If you’re a machine learning researcher or student, an engineer or developer, we’d love to hear your questions and feedback. Write us at machine-learning@apple.com

Apple Machine Learning Journal