Interpretations of Domain Adaptations via Layer Variational Analysis
This work addresses the problem of limited theoretical insights into transfer learning for researchers and practitioners, offering a novel framework and method, though it appears incremental as it builds on existing domain adaptation concepts.
The study tackled the lack of theoretical understanding of transfer learning in deep neural networks by developing a framework using layer variational analysis, which proved that transfer learning success can be guaranteed under certain data conditions and led to a new method that improved efficiency and accuracy in domain adaptation tasks, as validated by numerical experiments showing better performance than gradient descent.
Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method.