Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes

Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes

Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chee Seng Chan

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 825-831. https://doi.org/10.24963/ijcai.2020/115

This paper proposes a novel deep polarized network (DPN) for learning to hash, in which each channel in the network outputs is pushed far away from zero by employing a differentiable bit-wise hinge-like loss which is dubbed as polarization loss. Reformulated within a generic Hamming Distance Metric Learning framework [Norouzi et al., 2012], the proposed polarization loss bypasses the requirement to prepare pairwise labels for (dis-)similar items and, yet, the proposed loss strictly bounds from above the pairwise Hamming Distance based losses. The intrinsic connection between pairwise and pointwise label information, as disclosed in this paper, brings about the following methodological improvements: (a) we may directly employ the proposed differentiable polarization loss with no large deviations incurred from the target Hamming distance based loss; and (b) the subtask of assigning binary codes becomes extremely simple --- even random codes assigned to each class suffice to result in state-of-the-art performances, as demonstrated in CIFAR10, NUS-WIDE and ImageNet100 datasets.
Keywords:
Computer Vision: Big Data and Large Scale Methods
Machine Learning: Clustering
Data Mining: Big Data, Large-Scale Systems