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.
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[1702.01983] Face Aging With Conditional Generative Adversarial Networks

Face Aging With Conditional Generative Adversarial Networks  #DeepLearning

  • In the work, we propose the GAN-based method for automatic face aging.
  • Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person’s identity in the aged version of his/her face.
  • Abstract: It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity.
  • The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.
  • We introduce a novel approach for “Identity-Preserving” optimization of GAN’s latent vectors.

Continue reading “[1702.01983] Face Aging With Conditional Generative Adversarial Networks”