Target Robust Discriminant Analysis

Understand

In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data.

• Consequentially, the so-called source classifier, trained on the available labelled data, deteriorates on the test, or target, data.
• Domain adaptive classifiers aim to combat this problem, but typically assume some particular form of domain shift.
• Most are not robust to violations of domain shift assumptions and may even perform worse than their non-adaptive counterparts.