Domain Adaptation

Adapt Neural Networks with Gradient Reversal

A classifier can perform well in evaluation and then weaken after deployment because the inputs changed. Product photos may come from a new camera, support messages may use different vocabulary, or sensor readings may come from different hardware. Collecting labels for the new environment can be expensive even when unlabeled examples are easy to obtain. Domain-adversarial training addresses one version of this problem. It asks a feature extractor to support the prediction task while making the source and target domains difficult to distinguish. A gradient reversal layer makes those two goals trainable with ordinary backpropagation by reversing the domain classifier’s gradient before it reaches the feature extractor.