954 research outputs found
Deep Supervised Hashing using Symmetric Relative Entropy
By virtue of their simplicity and efficiency, hashing algorithms have achieved significant success on large-scale approximate nearest neighbor search. Recently, many deep neural network based hashing methods have been proposed to improve the search accuracy by simultaneously learning both the feature representation and the binary hash functions. Most deep hashing methods depend on supervised semantic label information for preserving the distance or similarity between local structures, which unfortunately ignores the global distribution of the learned hash codes. We propose a novel deep supervised hashing method that aims to minimize the information loss generated during the embedding process. Specifically, the information loss is measured by the Jensen-Shannon divergence to ensure that compact hash codes have a similar distribution with those from the original images. Experimental results show that our method outperforms current state-of-the-art approaches on two benchmark datasets
Deep Hashing Network for Unsupervised Domain Adaptation
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.Comment: CVPR 201
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
Hashing with binary autoencoders
An attractive approach for fast search in image databases is binary hashing,
where each high-dimensional, real-valued image is mapped onto a
low-dimensional, binary vector and the search is done in this binary space.
Finding the optimal hash function is difficult because it involves binary
constraints, and most approaches approximate the optimization by relaxing the
constraints and then binarizing the result. Here, we focus on the binary
autoencoder model, which seeks to reconstruct an image from the binary code
produced by the hash function. We show that the optimization can be simplified
with the method of auxiliary coordinates. This reformulates the optimization as
alternating two easier steps: one that learns the encoder and decoder
separately, and one that optimizes the code for each image. Image retrieval
experiments, using precision/recall and a measure of code utilization, show the
resulting hash function outperforms or is competitive with state-of-the-art
methods for binary hashing.Comment: 22 pages, 11 figure
CryptoKnight:generating and modelling compiled cryptographic primitives
Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware applicatio
Domain Adaptive Computational Models for Computer Vision
abstract: The widespread adoption of computer vision models is often constrained by the issue of domain mismatch. Models that are trained with data belonging to one distribution, perform poorly when tested with data from a different distribution. Variations in vision based data can be attributed to the following reasons, viz., differences in image quality (resolution, brightness, occlusion and color), changes in camera perspective, dissimilar backgrounds and an inherent diversity of the samples themselves. Machine learning techniques like transfer learning are employed to adapt computational models across distributions. Domain adaptation is a special case of transfer learning, where knowledge from a source domain is transferred to a target domain in the form of learned models and efficient feature representations.
The dissertation outlines novel domain adaptation approaches across different feature spaces; (i) a linear Support Vector Machine model for domain alignment; (ii) a nonlinear kernel based approach that embeds domain-aligned data for enhanced classification; (iii) a hierarchical model implemented using deep learning, that estimates domain-aligned hash values for the source and target data, and (iv) a proposal for a feature selection technique to reduce cross-domain disparity. These adaptation procedures are tested and validated across a range of computer vision applications like object classification, facial expression recognition, digit recognition, and activity recognition. The dissertation also provides a unique perspective of domain adaptation literature from the point-of-view of linear, nonlinear and hierarchical feature spaces. The dissertation concludes with a discussion on the future directions for research that highlight the role of domain adaptation in an era of rapid advancements in artificial intelligence.Dissertation/ThesisDoctoral Dissertation Computer Science 201
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