Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval. We study the problem of learning binary vector embeddings under a supervised setting, also known as hashing. We propose a novel supervised hashing method based on optimizing an information-theoretic quantity, mutual information. We show that optimizing mutual information can reduce ambiguity in the induced neighborhood structure in learned Hamming space, which is essential in obtaining high retrieval performance. To this end, we optimize mutual information in deep neural networks with minibatch stochastic gradient descent, with a formulation that maximally and efficiently utilizes available supervision. Experiments on four image retrieval benchmarks, including ImageNet, confirm the effectiveness of our method in learning high-quality binary embeddings for nearest neighbor retrieval.
As the ultimate goal of hashing is to preserve a neighborhood structure in the Hamming space, we propose an information-theoretic solution and directly quantify the neighborhood ambiguity of the generated binary embeddings using a mutual information based criterion. Information-theoretic measures have also been considered in past hashing studies. Notably, in @cite_68 an affinity matching formulation is proposed with a pairwise cross-entropy loss to penalize the discrepancy between pairwise Hamming similarities and the ground truth affinities. A similar cross-entropy loss is adopted by Zhu al @cite_17 in convolutional neural networks. To permit gradient based optimization, the binary embeddings are relaxed to continuous values and a quantization loss is added. Venkatesware al @cite_5 also consider the cross-entropy loss in an unsupervised domain adaptation setting. These simple affinity matching methods are different from our proposed solution, where we employ an information-theoretic measure to directly minimize neighborhood ambiguity: separating distance distributions between queries and their neighbor and non-neighbor sets. Our proposed mutual information objective is efficient to compute, amenable to batch learning, and leads to state-of-the-art results in standard retrieval benchmarks.
@cite_5 @cite_68 @cite_17
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In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation. Searching approximate nearest neighbors in large scale high dimensional data set has been a challenging problem. This paper presents a novel and fast algorithm for learning binary hash functions for fast nearest neighbor retrieval. The nearest neighbors are defined according to the semantic similarity between the objects. Our method uses the information of these semantic similarities and learns a hash function with binary code such that only objects with high similarity have small Hamming distance. The hash function is incrementally trained one bit at a time, and as bits are added to the hash code Hamming distances between dissimilar objects increase. We further link our method to the idea of maximizing conditional entropy among pair of bits and derive an extremely efficient linear time hash learning algorithm. Experiments on similar image retrieval and celebrity face recognition show that our method produces apparent improvement in performance over some state-of-the-art methods.