Leveraging supervised information can lead to superior retrieval performance
in the image hashing domain but the performance degrades significantly without
enough labeled data. One effective solution to boost the performance is to
employ generative models, such as Generative Adversarial Networks (GANs), to
generate synthetic data in an image hashing model. However, GAN-based methods
are difficult to train and suffer from mode collapse issue, which prevents the
hashing approaches from jointly training the generative models and the hash
functions. This limitation results in sub-optimal retrieval performance. To
overcome this limitation, we propose a novel framework, the generative
cooperative hashing network (CoopHash), which is based on the energy-based
cooperative learning. CoopHash jointly learns a powerful generative
representation of the data and a robust hash function. CoopHash has two
components: a top-down contrastive pair generator that synthesizes contrastive
images and a bottom-up multipurpose descriptor that simultaneously represents
the images from multiple perspectives, including probability density, hash
code, latent code, and category. The two components are jointly learned via a
novel likelihood-based cooperative learning scheme. We conduct experiments on
several real-world datasets and show that the proposed method outperforms the
competing hashing supervised methods, achieving up to 10% relative improvement
over the current state-of-the-art supervised hashing methods, and exhibits a
significantly better performance in out-of-distribution retrieval