Learning with Limited Data
Good machine learning is heavily dependent on good data. A few more good data-points is likely to be worth billions of model parameters.
However, sometimes we need to train models when data is limited. There are a number of strategies that we can try.
Zero-Shot and Few-Shot Learning
- Pattern Exploitative Training is a way to use a small number of examples to train text classifiers. It is technically an example of synthetic data generation.