Neural Market Trends

An introduction to Generative Adversarial Networks (with code in TensorFlow)

You can read this in one minute.

My Notes:

  • Generative models create data similiar to what they trained on
  • Training these models is very hard
  • One approach is using Generative Adversarial Networks (GANs)
  • Facebook’s Yann LeCun considers them “the most interesting idea in the last 10 years in ML”
  • What are the differences between Discrimitive and Generative models?
  • A discriminative model learns a function that maps the input data (x) to some desired output class label (y). In probabilistic terms, they directly learn the conditional distribution P(y|x)
  • A generative model tries to learn the joint probability of the input data and labels simultaneously, i.e. P(x,y). This can be converted to P(y|x) for classification via Bayes rule, but the generative ability could be used for something else as well, such as creating likely new (x, y) samples
  • GANs were first introduced in 2014
  • GANs are two competing neural network model
  • One takes noise and generates samples (generator)
  • The other recieves samples from generator and training data (discriminator)
  • Trick is to discern between them in training
  • Models then converge until the generated data is similar to actual trained data
  • They are being applied to image generation tasks
  • See article for Python Code using TensorFlow
  • Or visit Github (https://github.com/AYLIEN/gan-intro)

More information

If you want to learn more about GANs we recommend starting with the following publications:

  1. Generative Adversarial Networks
  2. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
  3. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
  4. Improved Techniques for Training GANs
Read the entire source article here.

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