Generative AI

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• AI Solver
• Generative Query Network (GQN)
• Discriminative vs. Generative
• Natural Language Generation (NLG)
• Generative Model | Wikipedia
• Synthetic Labeling
• Demos, generating...
  • Music: Generating Piano Music with Transformer | I. Simon, A. Huang, J. Engel, C. "Fjord" Hawthorne - Google on Colab play with pretrained Transformer models for piano music generation, based on the Music Transformer model
  • Faces: TF-Hub generative image model | The TensorFlow Hub Authors - Google use of a TF-Hub module based on a generative adversarial network (GAN). The module maps from N-dimensional vectors, called latent space, to RGB images.
  • 3D Objects: 3D Style Transfer | Google uses Lucid to implement style transfer from a textured 3D model and a style image onto a new texture for the 3D model by using a Differentiable Image Parameterization.

• Three main types:
  • Autoencoder (AE) / Encoder-Decoder
  • Sequence Models
    • Transformer
    • Recurrent Neural Network (RNN)
    • Sequence to Sequence (Seq2Seq)
  • Adversarial Networks
    • Conditional Adversarial Architecture (CAA)
    • Generative Adversarial Network (GAN)
    • Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
    • Context-Conditional Generative Adversarial Network (CC-GAN)

More formally, given a set of data instances X and a set of labels Y:

• Generative models capture the joint probability p(X, Y), or just p(X) if there are no labels.
• Discriminative models capture the conditional probability p(Y | X).

A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words.

Informally:

• Generative models can generate new data instances.
• Discriminative models discriminate between different kinds of data instances.