TY - JOUR AU - Li, Haoliang AU - Pan, Sinno Jialin AU - Wan, Renjie AU - Kot, Alex C. PY - 2019/07/17 Y2 - 2024/03/28 TI - Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v33i01.33018602 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4880 SP - 8602-8609 AB - <p>Heterogeneous Transfer Learning (HTL) aims to solve transfer learning problems where a source domain and a target domain are of heterogeneous types of features. Most existing HTL approaches either explicitly learn feature mappings between the heterogeneous domains or implicitly reconstruct heterogeneous cross-domain features based on matrix completion techniques. In this paper, we propose a new HTL method based on a deep matrix completion framework, where kernel embedding of distributions is trained in an adversarial manner for learning heterogeneous features across domains. We conduct extensive experiments on two different vision tasks to demonstrate the effectiveness of our proposed method compared with a number of baseline methods.</p> ER -