In this paper, we propose an adversarial multi-label variational hashing (AMVH) method to learn compact binary codes for efficient image retrieval. Unlike most existing deep hashing methods which only learn binary codes from specific real samples, our AMVH learns hash functions from both synthetic and real data which make our model effective for unseen data. Specifically, we design an end-to-end deep hashing framework which consists of a generator network and a discriminator-hashing network by enforcing simultaneous adversarial learning and discriminative binary codes learning to learn compact binary codes. The discriminator-hashing network learns binary codes by optimizing a multi-label discriminative criterion and minimizing the quantization loss between binary codes and real-value codes. The generator network is learned so that latent representations can be sampled in a probabilistic manner and used to generate new synthetic training sample for the discriminator-hashing network. Experimental results on several benchmark datasets show the efficacy of the proposed approach.

中文翻译：

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对抗式多标签变种哈希

在本文中，我们提出了一种对抗式多标签变分哈希（AMVH）方法，以学习紧凑的二进制代码来进行有效的图像检索。与大多数现有的仅从特定真实样本中学习二进制代码的深度哈希算法不同，我们的AMVH从合成数据和真实数据中学习哈希函数，这使我们的模型对看不见的数据有效。具体而言，我们通过强制同时进行对抗性学习和区分性二进制代码学习以学习紧凑型二进制代码，设计了一个由生成器网络和鉴别器哈希网络组成的端到端深度哈希框架。鉴别器哈希网络通过优化多标签鉴别标准并最小化二进制代码和实值代码之间的量化损失来学习二进制代码。学习生成器网络，以便可以以概率方式对潜在表示进行采样，并将其用于为鉴别器哈希网络生成新的综合训练样本。在几个基准数据集上的实验结果表明了该方法的有效性。