✳ BETWEEN QUESTIONS ML STUDY ATLAS

A QUESTION-LED LESSON

Latent Representations: Can Hidden Coordinates Describe Data?

Can a few hidden numbers capture useful variation in many observed values?

An internal coordinate

A latent representation is a set of variables used to describe underlying structure in a model. In an autoencoder, an encoder maps input into a representation and a decoder maps it back toward an output.

Move a coordinate

Our toy decoder maps z to [z, 2z]. Changing z moves along a line in two-dimensional output space. This is illustrative; learned latent spaces can be nonlinear and high-dimensional. Follow the latent coordinate through the fixed decoder to its two output coordinates.

INTERACTIVE EXPLORATION

Move a latent coordinate

Change z for the toy decoder output [z, 2z].

Interpretation needs care

A latent coordinate does not automatically correspond to a human concept such as color or size. Different models and training objectives can produce different representations.

Check your understanding

For z = 2 and decoder [z, 2z], what is the output?

[2, 4].

Keep exploring

What is a generative model? ↗What is a vector? ↗

Further reading

Source and further reading ↗