Neural Aesthetic w6 class notes

  • Generative models synthesize new samples that resemble the training data
    • applications: visual content generation, language models (chatbots, assistants, duplexes), music, etc
    • Models the probability distribution of all possible images; images that look like the dataset have a high probability
  • PCA projects down in lower dimensions and back out
  • latent space: space of all possible generated outputs
  • later layers can be used as a feature extractor because it is a compact but high-level representation
    • distance calculations between feature extractors can be used to determine similarities between images
    • transfer learning
    • can use PCA to reduce redundancies, then calculate distances
      • images (points) can then be embedded in feature space
        • vectors between points imply relationships
  • Autoencoders reconstruct its inputs as its outputs; networks learns an essential representation of the data via compression through a small middle layer
    • first half encoder, second half decoder
    • can throw in labels for a conditional distribution
    • can encode images and get their latent representation to project outward
      • smile vector
  • GANs: circa 2014
    • hard to train
    • hard to evaluate
    • can’t encode images directly
    • structured like a decoupled autoencoder
      • generator > discriminator
        • generator: basically like the decoder in an autoencoder
          • takes in random numbers, not images
          • tries to create images to trick the discriminator into thinking they’re real
        • discriminator: takes in an input image (from generator), decides if it is real or fake
        • “adversarial”: trained to work against each other
  • DC GANs
    • unsupervised technique, but can give it labels
      • interpolations through latent space AND through labels
        • labels are one hot vectors
        • MINST: glyph between integers
  • Deep Generator Network
    • similar to deep dream; optimizes an output image to maximize a class label
  • Progressively grown GANs
    • super high res, super realistic

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