An Improved Transfer Model: Randomized Transferable Machine
This work provides an incremental improvement for researchers and practitioners using feature-based transfer learning, particularly when small domain divergence remains after feature extraction.
This paper addresses the problem of small domain divergence in feature-based transfer learning, where existing methods assume domain-invariant features. The authors propose the Randomized Transferable Machine (RTM) which enlarges source training data by random corruptions, effectively training a model on infinitely corrupted source data populations via a marginalized solution. This approach theoretically and experimentally shows superior transfer performance.
Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is \emph{domain-invariant}, and thus train a transfer model $\mathcal{M}$ on the source domain. In this paper, we consider a more realistic scenario where the new feature representation is suboptimal and small divergence still exists across domains. We propose a new transfer model called Randomized Transferable Machine (RTM) to handle such small divergence of domains. Specifically, we work on the new source and target data learned from existing feature-based transfer methods. The key idea is to enlarge source training data populations by randomly corrupting the new source data using some noises, and then train a transfer model $\widetilde{\mathcal{M}}$ that performs well on all the corrupted source data populations. In principle, the more corruptions are made, the higher the probability of the new target data can be covered by the constructed source data populations, and thus better transfer performance can be achieved by $\widetilde{\mathcal{M}}$. An ideal case is with infinite corruptions, which however is infeasible in reality. We develop a marginalized solution that enables to train an $\widetilde{\mathcal{M}}$ without conducting any corruption but equivalent to be trained using infinite source noisy data populations. We further propose two instantiations of $\widetilde{\mathcal{M}}$, which theoretically show the transfer superiority over the conventional transfer model $\mathcal{M}$. More importantly, both instantiations have closed-form solutions, leading to a fast and efficient training process. Experiments on various real-world transfer tasks show that RTM is a promising transfer model.