DHLBT: Efficient Cross-Modal Hashing Retrieval Method Based on Deep Learning Using Large Batch Training

Cross-modal hashing has attracted considerable attention as it can implement rapid cross-modal retrieval through mapping data of different modalities into a common Hamming space. With the development of deep learning, more and more cross-modal hashing methods based on deep learning are proposed. However, most of these methods use a small batch to train a model. The large batch training can get better gradients and can improve training efficiency. In this paper, we propose the DHLBT method, which uses the large batch training and introduces orthogonal regularization to improve the generalization ability of the DHLBT model. Moreover, we consider the discreteness of hash codes and add the distance between hash codes and features to the objective function. Extensive experiments on three benchmarks show that our method achieves better performance than several existing hashing methods.

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Image Retrieval Method of Bayonet Vehicle Based on the Improvement of Deep Learning Network

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition ◽

10.1145/3430199.3430209 ◽

2020 ◽

Author(s):

Zilong Wang ◽

Ling Xiong ◽

Yang Chen

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Retrieval Method ◽

Learning Network ◽

Deep Learning Network

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Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Energies ◽

10.3390/en14154674 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4674

Author(s):

Qingsheng Zhao ◽

Juwen Mu ◽

Xiaoqing Han ◽

Dingkang Liang ◽

Xuping Wang

Keyword(s):

Deep Learning ◽

Power Consumption ◽

Detection Efficiency ◽

Evaluation Model ◽

Smart Meter ◽

Smart Meters ◽

Abnormal Detection ◽

Prediction Residual ◽

Threshold Setting ◽

Training Efficiency

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

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Representing Deep Neural Networks Latent Space Geometries with Graphs

Algorithms ◽

10.3390/a14020039 ◽

2021 ◽

Vol 14 (2) ◽

pp. 39

Author(s):

Carlos Lassance ◽

Vincent Gripon ◽

Antonio Ortega

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Objective Function ◽

Learning Process ◽

Deep Neural Networks ◽

State Of The Art ◽

The Core ◽

Learning Tasks ◽

Latent Space

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.

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Astrid

Proceedings of the VLDB Endowment ◽

10.14778/3436905.3436907 ◽

2020 ◽

Vol 14 (4) ◽

pp. 471-484

Author(s):

Suraj Shetiya ◽

Saravanan Thirumuruganathan ◽

Nick Koudas ◽

Gautam Das

Keyword(s):

Deep Learning ◽

Objective Function ◽

Pattern Matching ◽

Language Processing ◽

Language Model ◽

Language Models ◽

Selectivity Estimation ◽

Statistical Correlations ◽

Benchmark Datasets ◽

Traditional Approaches

Accurate selectivity estimation for string predicates is a long-standing research challenge in databases. Supporting pattern matching on strings (such as prefix, substring, and suffix) makes this problem much more challenging, thereby necessitating a dedicated study. Traditional approaches often build pruned summary data structures such as tries followed by selectivity estimation using statistical correlations. However, this produces insufficiently accurate cardinality estimates resulting in the selection of sub-optimal plans by the query optimizer. Recently proposed deep learning based approaches leverage techniques from natural language processing such as embeddings to encode the strings and use it to train a model. While this is an improvement over traditional approaches, there is a large scope for improvement. We propose Astrid, a framework for string selectivity estimation that synthesizes ideas from traditional and deep learning based approaches. We make two complementary contributions. First, we propose an embedding algorithm that is query-type (prefix, substring, and suffix) and selectivity aware. Consider three strings 'ab', 'abc' and 'abd' whose prefix frequencies are 1000, 800 and 100 respectively. Our approach would ensure that the embedding for 'ab' is closer to 'abc' than 'abd'. Second, we describe how neural language models could be used for selectivity estimation. While they work well for prefix queries, their performance for substring queries is sub-optimal. We modify the objective function of the neural language model so that it could be used for estimating selectivities of pattern matching queries. We also propose a novel and efficient algorithm for optimizing the new objective function. We conduct extensive experiments over benchmark datasets and show that our proposed approaches achieve state-of-the-art results.

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Deep Learning to Ternary Hash Codes by Continuation

10.36227/techrxiv.17083019.v1 ◽

2021 ◽

Author(s):

Mingrui Chen ◽

Weiyu Li ◽

weizhi lu

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Continuation Method ◽

Binary Codes ◽

Hard Thresholding ◽

Joint Learning ◽

Ternary Codes ◽

First Time ◽

Hash Codes ◽

Discrete Functions

Recently, it has been observed that $\{0,\pm1\}$-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform $\{-1, 1\}$-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes.

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A Clonal Selection Optimization System for Multiparty Secure Computing

Complexity ◽

10.1155/2021/7638394 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Minyu Shi ◽

Yongting Zhang ◽

Huanhuan Wang ◽

Junfeng Hu ◽

Xiang Wu

Keyword(s):

Deep Learning ◽

Large Scale ◽

Differential Privacy ◽

Clonal Selection ◽

Smart Cities ◽

Model Performance ◽

Processing System ◽

Optimization System ◽

Learning Scheme ◽

Training Efficiency

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.

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