Sample Complexity of Transfer Learning: An Optimal Transport Approach
Researchers have theoretically analyzed the benefits of transfer learning using an optimal transport framework. Their findings suggest that for data dimensions greater than three, transfer learning offers improved sample efficiency compared to direct learning, particularly for complex models with non-smooth activation functions. This theoretical advantage was numerically demonstrated using image classification tasks, showing significant performance gains in data-scarce scenarios. AI
IMPACT Provides theoretical backing for transfer learning's effectiveness in data-hungry AI models.