Supervised Domain Adaptation using Graph Embedding
Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target) Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of multi view graph embedding and dimensionality reduction. Instead of solving the generalised eigenvalue problem to perform the embedding, we formulate the graph preserving criterion as a loss in the neural network and learn a domain invariant feature transformation in an end to end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework which generalises the prior methods CCSA and d SNE, and enables simple and effective loss designs; an LDA inspired instantiation of the framework leads to performance on par with the state of the art on the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.
2020 25th International Conference on Pattern Recognition (ICPR)