Learning Discriminatively Reconstructed Source Data for Object Recognition with Few Examples

We aim at improving the object recognition with few training data in the target domain by leveraging abundant auxiliary data in the source domain. The major issue obstructing knowledge transfer from source to target is the limited correlation between the two domains. Transferring irrelevant information from the source domain usually leads to performance degradation in the target domain. To address this issue, we propose a transfer learning framework with the two key components, such as discriminative source data reconstruction and dual-domain boosting. The former correlates the two domains via reconstructing source data by target data in a discriminative manner. The latter discovers and delivers only knowledge shared by the target data and the reconstructed source data. Hence, it facilitates recognition in the target. The promising experimental results on three benchmarks of object recognition demonstrate the effectiveness of our approach.