DualHash: A Stochastic Primal-Dual Algorithm with Theoretical Guarantee for Deep Hashing
Luxuan Li, Xiao Wang, and Chunfeng Cui

TL;DR
DualHash introduces a stochastic primal-dual algorithm with theoretical guarantees for deep hashing, effectively handling discrete regularizations and improving retrieval performance with proven convergence bounds.
Contribution
It proposes a novel primal-dual hashing algorithm that transforms nonconvex regularization into the dual space, enabling closed-form solutions and providing convergence guarantees.
Findings
DualHash achieves superior retrieval accuracy on image databases.
The algorithm has complexity bounds of O(ε^{-4}) and O(ε^{-3}) with variance reduction.
Experiments validate the effectiveness of the proposed method.
Abstract
Deep hashing converts high-dimensional feature vectors into compact binary codes, enabling efficient large-scale retrieval. A fundamental challenge in deep hashing stems from the discrete nature of quantization in generating the codes. W-type regularizations, such as , have been proven effective as they encourage variables toward binary values. However, existing methods often directly optimize these regularizations without convergence guarantees. While proximal gradient methods offer a promising solution, the coupling between W-type regularizers and neural network outputs results in composite forms that generally lack closed-form proximal solutions. In this paper, we present a stochastic primal-dual hashing algorithm, referred to as DualHash, that provides rigorous complexity bounds. Using Fenchel duality, we partially transform the nonconvex W-type regularization optimization…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Data Compression Techniques · Image Retrieval and Classification Techniques
