 Representation learning

Representation learning learns a transformation of observations into features that make a subsequent task easier. Writing $z=f_{\theta }(x)$ and a prediction as $g_{\varphi }(z)$ separates the learned representation from the predictor; training may optimize both together. The objective can come from supervised learning or unsupervised learning.

For example, a neural network trained to classify images can learn edge-like and shape-sensitive features instead of receiving a fixed list of hand-measured quantities. The representation can sometimes be reused on a second task with fewer labeled examples. Such transfer is a hypothesis to evaluate: information useful for the original task can omit a distinction essential to the next. A distributed representation describes how information is encoded; representation learning describes how the encoding is obtained.^[ref]