7 Auto Encoding and Self Supervision
This chapter covers
- Training without labels.
- Auto Encoding to project data.
- Constraining networks with bottlenecks.
- Adding noise to to improve performance.
- Predicting the next item to make generative models.
At this point we have learned about a number of different approaches to specifying a neural network, and we have done this for classification and regression problems. These are the classic machine learning problems, where for each data point x (e.g., a picture of a fruit) we have an associated answer y (e.g., fresh or rotten). But what if we do not have a label y? Is there any useful way for us to learn? You should recognize this as an unsupervised learning scenario.