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Autoencoders

• Autoencoders are neural models that take an input and reconstruct the same input in their output.
  • The reconstruction will not be perfect
  • As a result the autoencoder is “lossy”

• The autoencoder architecture compresses the information in the input to reach a “latent” space.

• Autoencoders are considered to be trained in an unsupervised fashion.

• The latent space (or code, or bottleneck)
  • contains all the information necessary to reconstruct the input.
  • can be considered as a set of features that are not correlated to each other.

• In contrast, the input may be full of correlated features.

• Examples autoencoder applications include:
  • data denoising
  • segmentation
  • anomaly detection
  • feature generation

Homework 10b (due April 8)

• Make all revisions requested by the instructor to your final project proposal.

Homework 11a (due April 19)

• Next class we will have a guest speaker
  • Magdalena Fuentes (NYU postdoctoral fellow) will talk to us about her research.
  • She will focus her presentation on these papes:
    • TBD
    • TBD
  • Read Magdalena’s papers in detail
  • On our course subreddit, there is a thread to discuss Magdalena’s papers.
  • You must post two questions you have after reading the paper.
  • Do not repeat questions that have already been asked.
  • The sooner you post, the less likely it will be that somebody else already asked your question.
  • You must also try to answer at least one of the questions asked by your peers.

Homework 11b: show us your data

Due April 19th at 11:59PM (Eastern Standard Time)

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