Binary code ≈ Binary codeBinary code
View article: Binarized Neural Networks
Binarized Neural Networks Open
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at train-time. We conduct two sets of experiments, each based on …
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SOK: (State of) The Art of War: Offensive Techniques in Binary Analysis Open
Finding and exploiting vulnerabilities in binary code is a challenging task. The lack of high-level, semantically rich information about data structures and control constructs makes the analysis of program properties harder to scale. Howev…
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Deep Hashing Network for Efficient Similarity Retrieval Open
Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing, which improves the quality of hash coding by exploiting the sema…
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Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Open
Product Quantization (PQ) has long been a mainstream for generating an\nexponentially large codebook at very low memory/time cost. Despite its success,\nPQ is still tricky for the decomposition of high-dimensional vector space, and\nthe re…
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BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 Open
We introduce BinaryNet, a method which trains DNNs with binary weights and activations when computing parameters' gradient. We show that it is possible to train a Multi Layer Perceptron (MLP) on MNIST and ConvNets on CIFAR-10 and SVHN with…
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Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval Open
Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to learn compact binary codes that construct the underlying corr…
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Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization Open
Reverse engineering is a manually intensive but necessary technique for understanding the inner workings of new malware, finding vulnerabilities in existing systems, and detecting patent infringements in released software. An assembly clon…
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Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder Open
Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in s…
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Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection Open
Binary code similarity detection, whose goal is to detect similar binary functions without having access to the source code, is an essential task in computer security. Traditional methods usually use graph matching algorithms, which are sl…
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Binary Generative Adversarial Networks for Image Retrieval Open
The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversarial networks (BGAN) to embed images to binary codes in an u…
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Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification Open
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Deep Graph-neighbor Coherence Preserving Network for Unsupervised Cross-modal Hashing Open
Unsupervised cross-modal hashing (UCMH) has become a hot topic recently. Current UCMH focuses on exploring data similarities. However, current UCMH methods calculate the similarity between two data, mainly relying on the two data's cross-m…
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Deep Supervised Discrete Hashing Open
With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefit from recent advances in deep learning, deep hashing methods have achieved promising results f…
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DeepBinDiff: Learning Program-Wide Code Representations for Binary Diffing Open
Binary diffing analysis quantitatively measures the differences between two given binaries and produces fine-grained basic block level matching.It has been widely used to enable different kinds of critical security analysis.However, all ex…
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Latent Semantic Minimal Hashing for Image Retrieval Open
Hashing-based similarity search is an important technique for large-scale query-by-example image retrieval system, since it provides fast search with computation and memory efficiency. However, it is a challenge work to design compact code…
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Deep Binary Reconstruction for Cross-Modal Hashing Open
To satisfy the huge storage space and organization capacity requirements in addressing big multimodal data, hashing techniques have been widely employed to learn binary representations in cross-modal retrieval tasks. However, optimizing th…
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An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing Open
Machine learning methods can be used for solving important binary classification tasks in domains such as display advertising and recommender systems. In many of these domains categorical features are common and often of high cardinality. …
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Binary code is not easy Open
Binary code analysis is an enabling technique for many applications. Modern compilers and run-time libraries have introduced significant complexities to binary code, which negatively affect the capabilities of binary analysis tool kits to …
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Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features Open
Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-cra…
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jTrans: jump-aware transformer for binary code similarity detection Open
Binary code similarity detection (BCSD) has important applications in various fields such as vulnerabilities detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can co…
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Graph Convolutional Network Hashing for Cross-Modal Retrieval Open
Deep network based cross-modal retrieval has recently made significant progress. However, bridging modality gap to further enhance the retrieval accuracy still remains a crucial bottleneck. In this paper, we propose a Graph Convolutional H…
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High-speed three-dimensional shape measurement based on cyclic complementary Gray-code light Open
The binary defocusing technique has been widely used in high-speed three-dimensional (3D) shape measurement because it breaks the bottlenecks in high-speed fringe projection and the projector's nonlinear response. However, it is challengin…
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Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes Open
This paper proposes a novel deep polarized network (DPN) for learning to hash, in which each channel in the network outputs is pushed far away from zero by employing a differentiable bit-wise hinge-like loss which is dubbed as polarization…
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Medical Image Retrieval Based on Convolutional Neural Network and Supervised Hashing Open
In recent years, with extensive application in image retrieval and other tasks, a convolutional neural network (CNN) has achieved outstanding performance. In this paper, a new content-based medical image retrieval (CBMIR) framework using C…
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Deep Unsupervised Image Hashing by Maximizing Bit Entropy Open
Unsupervised hashing is important for indexing huge image or video collections without having expensive annotations available. Hashing aims to learn short binary codes for compact storage and efficient semantic retrieval. We propose an uns…
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Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval Open
In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recentl…
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Double-Bit Quantization for Hashing Open
Hashing, which tries to learn similarity-preserving binary codes for data representation, has been widely used for efficient nearest neighbor search in massive databases due to its fast query speed and low storage cost. Because it is NP ha…
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Binary Generative Adversarial Networks for Image Retrieval Open
The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversarial networks (BGAN) to embed images to binary codes in an u…
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Sequential Discrete Hashing for Scalable Cross-Modality Similarity Retrieval Open
With the dramatic development of the Internet, how to exploit large-scale retrieval techniques for multimodal web data has become one of the most popular but challenging problems in computer vision and multimedia. Recently, hashing methods…
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HashNet: Deep Learning to Hash by Continuation Open
Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep learning to hash, which improves retrieval quality by end-t…