Guided Adversarial Robust Transfer Learning with Source Mixing

Understand

Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on a new one, reducing the need for extensive data and training in each new context.

• Many existing transfer learning methods rely on leveraging information from source populations closely resembling the target population.
• However, this approach often overlooks valuable knowledge that may be present in different yet potentially related auxiliary samples.
• When dealing with a limited amount of target data and multiple source data, we introduce a novel approach, Guided Adversarial Robust Transfer (GART) learning, that breaks free from strict similarity constraints.