Transfer Hashing with Privileged Information
This work addresses data scarcity in hashing for real-world applications, representing an incremental advancement by adapting existing methods with transfer learning.
The paper tackles the problem of data sparsity in learning to hash by proposing a transfer learning framework called Transfer Hashing with Privileged Information (THPI), which extends Iterative Quantization (ITQ) to ITQ+ and LapITQ+ using auxiliary data, achieving improved performance verified through experiments on benchmark datasets.
Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However, this assumption cannot be satisfied in some real-world applications. To address this data sparsity issue in hashing, inspired by transfer learning, we propose a new framework named Transfer Hashing with Privileged Information (THPI). Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+. In ITQ+, a new slack function is learned from auxiliary data to approximate the quantization error in ITQ. We developed an alternating optimization approach to solve the resultant optimization problem for ITQ+. We further extend ITQ+ to LapITQ+ by utilizing the geometry structure among the auxiliary data for learning more precise binary codes in the target domain. Extensive experiments on several benchmark datasets verify the effectiveness of our proposed approaches through comparisons with several state-of-the-art baselines.