neural aesthetic class notes


  • data points are combinations inside feature space
  • Embeddings give us relationships between data points (closer points are more similar)
  • magnitude and direction have meaning, allow many basic retrieval applications
  • feature vectors and latent spaces are examples of embeddings
  • two vectors between two pairs of points have meaning

features are patterns of activations

  • every layer becomes less abstract/ more specific: edges, parallel lines, shapes, categories
  • last layer of activations; distance or correlation between vectors

transfer learning with images

  • dimensionality reduction; tries to preserve geometries
  • linearly-independent components


  • man>woman; country>capital; singular>plural
  • words are units; sentences are infinite—sentences and paragraphs can be embedded in feature space
    • word vectors are learned implicitly
    • question-inversion vector

principle component analysis to reduce

t-SNE better for visualization and discovery of similar neighbors, but for smaller datasets;

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