面向最近邻搜索的稀疏归纳生成对抗哈希方法
Sparse-Inductive Generative Adversarial Hashing for Nearest Neighbor Search
AI总结：
针对无监督哈希中异构空间映射的分布不一致与优化难题，提出SiGAH方法，通过生成对抗框架与压缩感知机制学习哈希码，在四个基准数据集上性能超越现有最优方法。
摘要：
近十年来，无监督哈希受到了广泛的研究关注，其通常旨在在汉明空间中保留预定义的度量（即欧氏度量）。为此，现有哈希方法的编码函数通常是拟等距的，致力于减少从目标度量空间到离散汉明空间的量化损失。然而，直接最小化这类误差实际上存在问题，因为上述两个度量空间是异构的，且拟等距映射是非线性的。前者会导致特征分布不一致，后者则会带来优化难题。\n本文提出了一种新型无监督哈希方法，名为Sparsity-Induced Generative Adversarial Hashing（SiGAH，稀疏诱导生成对抗哈希），可将大规模高维特征编码为二进制码，通过生成对抗训练框架很好地解决了上述两个问题。我们的核心创新点并非最小化量化损失，而是通过生成模型强制学习到的汉明空间与目标度量空间具有相似的数据分布。具体而言，我们将基于ReLU的神经网络作为生成器来输出二进制码，将基于MSE损失的自编码器网络作为判别器，在此基础上开展生成对抗学习以训练哈希函数。此外，为了从哈希码生成合成特征，我们在生成模型中引入了压缩感知流程，强制二进制码的重构边界与原始特征的重构边界保持一致。最后，该生成对抗框架可通过Adam优化器进行训练。\n在Tiny100K、GIST1M、Deep1M和MNIST四个基准数据集上的实验结果表明，所提出的SiGAH方法性能优于现有最优方法。
英文摘要：
Unsupervised hashing has received extensive research focus on the past decade, which typically aims at preserving a predefined metric (i.e. Euclidean metric) in the Hamming space. To this end, the encoding functions of the existing hashing are typically quasi-isometric, which devote to reducing the quantization loss from the target metric space to the discrete Hamming space. However, it is indeed problematic to directly minimize such error, since such mentioned two metric spaces are heterogeneous, and the quasi-isometric mapping is non-linear. The former leads to inconsistent feature distributions, while the latter leads to problematic optimization issues. In this paper, we propose a novel unsupervised hashing method, termed Sparsity-Induced Generative Adversarial Hashing (SiGAH), to encode large-scale high-dimensional features into binary codes, which well solves the two problems through a generative adversarial training framework. Instead of minimizing the quantization loss, our key innovation lies in enforcing the learned Hamming space to have similar data distribution to the target metric space via a generative model. In particular, we formulate a ReLU-based neural network as a generator to output binary codes and an MSE-loss based auto-encoder network as a discriminator, upon which a generative adversarial learning is carried out to train hash functions. Furthermore, to generate the synthetic features from the hash codes, a compressed sensing procedure is introduced into the generative model, which enforces the reconstruction boundary of binary codes to be consistent with that of original features. Finally, such generative adversarial framework can be trained via the Adam optimizer. Experimental results on four benchmarks, i.e., Tiny100K, GIST1M, Deep1M, and MNIST, have shown that the proposed SiGAH has superior performance over the state-of-the-art approaches.
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