Generative approach learns hash functions for efficient binary search.
problem Challenges in learning discrete hash functions for fast search.
method Generative approach using Minimum Description Length principle and stochastic distributional gradient.
result Significantly better retrieval results than existing methods.
Fuzzy hashes learn from data to improve file similarity detection.
problem Measuring similarity between files, especially malware.
method Learned fuzzy hashes using a minimax training framework.
result Learned fuzzy hashes outperform traditional methods for file similarity.
The paper compares different hashing schemes for their performance on structured data.
problem The performance of hashing schemes on structured input is not well understood.
method The paper compares mixed tabulation hashing, multiply-mod-prime hashing, and MurmurHash3.
result Mixed tabulation hashing performs similarly to truly random hashing but is faster and has a proven guarantee.
ADSH uses asymmetric deep hashing for faster training and better performance.
problem Efficiently utilizing supervised information for large-scale database in deep supervised hashing.
method ADSH learns a deep hash function only for query points and directly learns hash codes for database points.
result ADSH achieves state-of-the-art performance in real applications.
FlexCMH learns effective hashing codes from weakly-paired data.
problem Cross-modal hashing assumes perfect correspondence between samples, which is unrealistic.
method FlexCMH uses clustering-based matching to find potential correspondence and jointly optimizes it with hashing functions.
result FlexCMH achieves significantly better results than state-of-the-art methods.
New hashing method improves document retrieval precision.
problem Efficiently retrieving similar documents from large text databases.
method Pairwise supervised hashing with Bernoulli VAE and unbiased gradient estimator.
result Superior performance compared to existing methods.
In supervised binary hashing, one wants to learn a function that maps a high-dimensional feature vector to a vector of binary codes, for application to fast image retrieval. This typically results in a difficult optimization problem, nonconvex and nonsmooth, because of the discrete variables involved. Much work has sim…
Hyperplane hashing aims at rapidly searching nearest points to a hyperplane, and has shown practical impact in scaling up active learning with SVMs. Unfortunately, the existing randomized methods need long hash codes to achieve reasonable search accuracy and thus suffer from reduced search speed and large memory overhe…
Hash codes are a very efficient data representation needed to be able to cope with the ever growing amounts of data. We introduce a random forest semantic hashing scheme with information-theoretic code aggregation, showing for the first time how random forest, a technique that together with deep learning have shown spe…
LSH methods extend to function spaces for efficient similarity search.
problem Efficient similarity search in function spaces.
method Locality-sensitive hashing (LSH) extended to Lp spaces using function approximation or Monte Carlo techniques. result An LSH family for Wasserstein distance over continuous probability distributions.
A new hashing method improves accuracy by learning an orthogonal transform.
problem Improving hashing accuracy for similarity search.
method Procrustean Orthogonal Sparse Hashing (POSH) method.
result POSH outperforms state-of-the-art hashing methods.
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 approac…
Binary hashing is a well-known approach for fast approximate nearest-neighbor search in information retrieval. Much work has focused on affinity-based objective functions involving the hash functions or binary codes. These objective functions encode neighborhood information between data points and are often inspired by…
Improves two-stage hashing methods for better image retrieval.
problem Developing efficient binary codes for image retrieval.
method Theoretical analysis and empirical improvements of two-stage hashing methods using high-capacity hash functions.
result Proposes a novel two-stage hashing method significantly outperforming previous studies.
Paper introduces EM-KSH and EM-SPLH methods for supervised hashing.
problem Efficiently optimize retrieval speed and storage cost while preserving semantic information.
method Convert supervised hashing formulations to CRF, solve consistency equations using linear approximation of sigmoid function.
result Experimental results show superior performance of EM-KSH and EM-SPLH.
New neural network outperforms existing methods in scene matching.
problem Automated scene matching with high accuracy and low false positives.
method Convolutional hashing using a new loss function and training scheme.
result Significantly higher true positive rate and 100-fold reduction in false positives.
New loss function and training scheme improve binary hash codes for better similarity search.
problem Improving binary hash codes for better similarity search tasks.
method Log likelihood loss on Hamming distance target, novel training scheme, multi-indexing.
result Significant improvements in MAP (84%) and query cost reduction for ImageNet and SIFT 1M.
A new hashing method handles complex multi-level labels.
problem Handling complex multi-level labels in cross-modal data retrieval.
method Derives a semantic ranking list from feature and label information, integrates semantic ranking into deep cross-modal hashing.
result RDCMH outperforms other methods in cross-modal retrieval applications.
A new sketching method reduces tensor memory usage and enables efficient tensor operations.
problem Efficiently compressing and retaining tensor structure in large datasets.
method Higher-order Count Sketch (HCS) using multiple hash functions and tensor products.
result HCS achieves significant memory savings and efficient tensor operations.
ForestHash combines random forests and CNNs for efficient data hashing.
problem Efficiently hashing large datasets while preserving similarity.
method Random forests with light-weight CNNs, grouping classes, and information-theoretic aggregation.
result Significantly outperforms state-of-the-art hashing methods for image retrieval.
A novel hash learning approach using codewords in Hamming space.
problem Hash learning for supervised, unsupervised, and semi-supervised scenarios.
method Uses codewords inferred from data to capture grouping aspects of hash codes, with regularization for automatic codeword selection. Solves via Block Coordinate Descent and SVM.
result Demonstrates superior performance in content-based image retrieval.
A new hashing framework learns multiple hash codes for each image to improve hash bucket search efficiency.
problem Existing hashing methods fail to handle complex image retrieval scenarios efficiently.
method Multiple Code Hashing (MCH) framework with deep reinforcement learning.
result Significant improvement in hash bucket search performance compared to single-code methods.
New LSH algorithms improve nearest-neighbor search performance.
problem Efficiently searching for similar high-dimensional data.
method High-dimensional locality-sensitive hashing (LSH) based on fruit fly olfactory circuit.
result New LSH algorithms outperform existing methods on benchmark datasets.
Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However, this assumption cannot be satisfied in some real-world applications. To address this data sparsity issue in hashing, inspired by transfer …
A Bloom filter approach combined with Transformer models improves accuracy for machine learning tasks on opaque IDs.
problem Improving accuracy for machine learning tasks on opaque IDs with large vocabulary sizes.
method Applying hash functions to map opaque IDs to multiple hash tokens, similar to a Bloom filter, and using a multi-layer Transformer to process these digests.
result Models outperform those without hashing and sampled softmax, achieving high accuracy with a smaller computational budget.
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with…
Efficient Discrete Supervised Hashing improves cross-modal retrieval by preserving semantic correlations and reducing quantization error.
problem Challenges in preserving semantic correlations and reducing quantization error in cross-modal hashing for large-scale retrieval.
method Collective matrix factorization on heterogenous features and semantic embedding with class labels to learn hash codes efficiently.
result EDSH produces superior performance in both accuracy and scalability over existing methods.
FSDH is a fast supervised hashing method that outperforms existing methods.
problem Efficiently hashing large datasets for fast retrieval.
method FSDH uses a simple regression of class labels to hash codes, avoiding iterative discrete optimization.
result FSDH is significantly faster (12-151x) than existing methods like SDH and FastHash.
FSL-BM improves real-time classification with fuzzy logic and binary meta-features.
problem Real-time classification accuracy, memory consumption, and time complexity.
method FSL-BM integrates fuzzy logic, binary meta-features, Hamming Distance, and Hash function for efficient supervised learning.
result FSL-BM provides faster and more accurate real-time classification compared to existing algorithms.
We generated a dataset of 200 GB with 10^9 features, to test our recent b-bit minwise hashing algorithms for training very large-scale logistic regression and SVM. The results confirm our prior work that, compared with the VW hashing algorithm (which has the same variance as random projections), b-bit minwise hashing i…
DSHL generates music using segment concatenation and hash learning.
problem Music generation using deep learning.
method Deep recurrent neural network and ranking-based hash learning.
result DSHL generates original and enjoyable music.
BioHash improves similarity search performance using sparse high-dimensional hash codes.
problem Improving similarity search performance in high-dimensional data.
method BioHash produces sparse high-dimensional hash codes through a data-driven approach based on synaptic plasticity.
result BioHash outperforms previous hashing methods in various similarity search tasks.
Minwise hashing is the standard technique in the context of search and databases for efficiently estimating set (e.g., high-dimensional 0/1 vector) similarities. Recently, b-bit minwise hashing was proposed which significantly improves upon the original minwise hashing in practice by storing only the lowest b bits of e…
Enhances hashing for fast retrieval with correlated bits.
problem Fast retrieval and small memory footprint for large-scale information retrieval.
method Employing Boltzmann machine distribution as variational posterior to model correlations among hash code bits.
result Significant performance gains achieved by effectively modeling correlations among hash code bits.
ForestDSH hashes improve nearest neighbor search in high-dimensional data.
problem High-dimensional classification and nearest neighbor search.
method Distribution-sensitive hashing using a forest of decision trees.
result ForestDSH hashes outperform LSH and state-of-the-art methods in speed and accuracy.
New hashing methods improve entity resolution in conflict datasets.
problem Improving entity resolution in databases with unknown unique identifiers.
method Introducing and comparing new locality sensitive hashing methods.
result New hashing methods outperform existing approaches in conflict datasets.
The paper develops methods to optimize ranking metrics for hashing.
problem Improving hashing for better retrieval performance.
method Developed tie-aware learning to rank formulations for hashing.
result Established new state-of-the-art for image retrieval by Hamming ranking.
Framework enhances DNNs against malware attacks.
problem Adversarial malware detection in DNNs.
method Hashing Transformation Deep Neural Networks (HashTran-DNN) with DAE regularizer.
result HashTran-DNN effectively defends against all four known attacks.
Enhances hashing for cross-modal retrieval using multi-view features.
problem Limited improvement in single-view hashing for cross-modal retrieval.
method Exploits multiple views to enrich feature information, learning discriminative hash codes.
result Superior performance compared to state-of-the-art methods on various datasets.
SDHR improves data hashing for better classification accuracy.
problem Efficient data hashing for high-dimensional data retrieval.
method Supervised Discrete Hashing with Relaxation (SDHR) using optimized regression targets.
result SDHR outperforms traditional methods in classification accuracy.
A new hashing method handles large-scale data with flexible similarity measures.
problem Efficient nearest neighbour search in large-scale systems with variable labels.
method End-to-end trainable network transforming data to uniform distribution on product of spheres, then hashing to binary form maximizing entropy.
result Outperforms baseline approaches in limited capacity regime.
In this paper, we propose to (seamlessly) integrate b-bit minwise hashing with linear SVM to substantially improve the training (and testing) efficiency using much smaller memory, with essentially no loss of accuracy. Theoretically, we prove that the resemblance matrix, the minwise hashing matrix, and the b-bit minwise…
FROSH and DFROSH speed up online sketching hashing for big data.
problem Efficiency and scalability of hashing methods for streaming data.
method Online sketching hashing (OSH) and FasteR Online Sketching Hashing (FROSH) algorithm.
result FROSH reduces training time and maintains sketching precision.
Hashing detects anomalies in structured data efficiently.
problem Identifying non-conforming samples on low-dimensional manifolds.
method Locality Sensitive Hashing in Preference Space.
result State-of-the-art performance at lower computational cost.
Paper reduces recommender system model size by 90%.
problem Large model size in recommender systems.
method Hybrid frequency and double hashing for model size reduction.
result 90% reduction in model size with no performance loss.
A new DP algorithm improves privacy in hashing and sampling for search and learning.
problem Improving privacy in hashing and sampling for large-scale applications.
method Combines differential privacy with one permutation hashing and bin-wise consistent weighted sampling.
result Proposes DP-OPH and DP-BCWS algorithms that enhance privacy while maintaining utility.
We present the first provably sublinear time algorithm for approximate \emph{Maximum Inner Product Search} (MIPS). Our proposal is also the first hashing algorithm for searching with (un-normalized) inner product as the underlying similarity measure. Finding hashing schemes for MIPS was considered hard. We formally sho…
Integration is affected by the curse of dimensionality and quickly becomes intractable as the dimensionality of the problem grows. We propose a randomized algorithm that, with high probability, gives a constant-factor approximation of a general discrete integral defined over an exponentially large set. This algorithm r…