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