Recently, unsupervised deep hashing has attracted increasing attention, mainly because of its potential ability to learn binary codes without identity annotations. However, because the labels are predicted by their pretext tasks, unsupervised deep hashing becomes unstable when learning with noisy labels. To mitigate this issue, we propose a simple but effective approach to self-supervised hash learning based on dual pseudo agreement. By adding a consistency constraint, our method can prevent corrupted labels and encourage generalization for effective knowledge distillation. Specifically, we use the refined pseudo labels as a stabilization constraint to train hash codes, which can implicitly encode semantic structures of the data into the learned Hamming space. Based on the stable pseudo labels, we propose a self-supervised hashing method with mutual information and noise contrastive loss. Throughout the process of hash learning, the stable pseudo labels and data distributions collaboratively work together as teachers to guide the binary codes learning process. Extensive experiments on three publicly available datasets demonstrate that the proposed method can consistently outperform state-of-the-art methods by large margins.

中文翻译：

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双伪协议对比自监督哈希

最近，无监督深度哈希引起了越来越多的关注，主要是因为它具有学习没有身份注释的二进制代码的潜在能力。然而，由于标签是由它们的借口任务预测的，当使用嘈杂的标签学习时，无监督的深度散列变得不稳定。为了缓解这个问题，我们提出了一种简单而有效的基于双伪协议的自监督哈希学习方法。通过添加一致性约束，我们的方法可以防止标签损坏并鼓励有效知识提炼的泛化。具体来说，我们使用细化的伪标签作为稳定约束来训练哈希码，它可以将数据的语义结构隐式编码到学习的汉明空间中。基于稳定的伪标签，我们提出了一种具有互信息和噪声对比损失的自监督哈希方法。在整个哈希学习过程中，稳定的伪标签和数据分布协同工作，作为教师指导二进制代码的学习过程。在三个公开可用的数据集上进行的大量实验表明，所提出的方法可以始终在很大程度上优于最先进的方法。