Auto-Encoding Twin-Bottleneck Hashing

TL;DR

This paper introduces a novel auto-encoding twin-bottleneck hashing framework that adaptively learns binary codes and data relevance graphs simultaneously, improving retrieval performance without relying on pre-computed graphs.

Contribution

It proposes a code-driven, auto-encoding model with twin bottlenecks that collaboratively optimize binary codes and data relevance graphs in an end-to-end manner.

Findings

Outperforms state-of-the-art hashing methods on benchmark datasets.

Effectively learns discriminative binary codes without pre-computed graphs.

Optimized via gradient descent, respecting binary constraints.

Abstract

Conventional unsupervised hashing methods usually take advantage of similarity graphs, which are either pre-computed in the high-dimensional space or obtained from random anchor points. On the one hand, existing methods uncouple the procedures of hash function learning and graph construction. On the other hand, graphs empirically built upon original data could introduce biased prior knowledge of data relevance, leading to sub-optimal retrieval performance. In this paper, we tackle the above problems by proposing an efficient and adaptive code-driven graph, which is updated by decoding in the context of an auto-encoder. Specifically, we introduce into our framework twin bottlenecks (i.e., latent variables) that exchange crucial information collaboratively. One bottleneck (i.e., binary codes) conveys the high-level intrinsic data structure captured by the code-driven graph to the other (i.e., continuous variables for low-level detail information), which in turn propagates the updated network feedback for the encoder to learn more discriminative binary codes. The auto-encoding learning objective literally rewards the code-driven graph to learn an optimal encoder. Moreover, the proposed model can be simply optimized by gradient descent without violating the binary constraints. Experiments on benchmarked datasets clearly show the superiority of our framework over the state-of-the-art hashing methods. Our source code can be found at https://github.com/ymcidence/TBH.