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Claim this profileWorks137 from public data
- A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications1,202
The motivations, mathematical representations, and structures of most GAN algorithms are introduced in detail, and they are compared to compare their commonalities and differences.
- A Survey on Self-Supervised Learning: Algorithms, Applications, and Future Trends638
A detailed introduction to the motivations behind most SSL algorithms is provided and their commonalities and differences are compared, as well as representative applications of SSL in domains such as image processing, computer vision, and natural language processing.
- Feature Selection Based on Structured Sparsity: A Comprehensive Study335
This paper compares the differences and commonalities of these methods based on regression and regularization strategies, but also provides useful guidelines to practitioners working in related fields to guide them how to do feature selection.
- Fast Supervised Discrete Hashing272
This paper proposes a new learning-based hashing method called "fast supervised discrete hashing" (FSDH) based on “supervised discrete hashing” (SDH), which uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm.
- Discriminant sparse neighborhood preserving embedding for face recognition264
DSNPE not only preserves the sparse reconstructive relationship of SNPE, but also sufficiently utilizes the global discriminant structures from the following two aspects: maximum margin criterion (MMC) is added into the objective function of DSNPE.
- Face recognition via Weighted Sparse Representation231
A locality Weighted Sparse Representation based Classification (WSRC) method is proposed, which utilizes both data locality and linearity; it can be regarded as extensions of SRC, but the coding is local.
- Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data227
A robust manifold embedding technique for assessing the reliability of interactions and predicting new interactions, which purely utilizes the topological information of PPI networks and can work on a sparse input protein interactome without requiring additional information types.
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- Prediction of Protein-Protein Interactions from Protein Sequence Using Local Descriptors175
Given the complex nature of PPIs, the performance of the proposed sequence-based method is promising, and it can be a helpful supplement for PPIs prediction.
- A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning117
This article provides a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing, and discusses the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used.
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- Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification106
A novel large margin multi- modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification that utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality.
- Locality preserving discriminant projections for face and palmprint recognition101
The proposed LPDP combining manifold criterion and Fisher criterion has more discriminanting power, and is more suitable for recognition tasks than LPP, which considers only the local information for classification tasks.
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- miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase98
The Bioconductor package miRBaseConverter and the Shiny-based web application are presented to provide a suite of functions for converting and retrieving miRNA name, accession, sequence, species, version and family information in different versions of miRBase.
- Supervised Discrete Hashing With Relaxation95
Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible.
- Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey93
This paper illustrates the development of binary hashing research by proposing an overall and clear classification of them, and conducts extensive experiments to compare the performance of these methods on five famous and public data sets.
- A Semi-Supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint92
A semi-supervised model for real-world nighttime dehazing that combines a pseudo-label-based retraining strategy and a local window-based brightness loss for semi-supervised training process is designed to suppress haze and glow while achieving realistic brightness.
- Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization92
A convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems is proposed and the impact of the imperfect CSI on the performance of this method is explored.
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- Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction90
Experiments showed that the proposed ensemble of probabilistic neural network (PNN) and neighborhood rough set model based gene reduction approach to tumor classification can obtain both high and stable classification performance, which is not too sensitive to the number of initially selected genes and competitive to most existing methods.
- Multi-Grained Attention Networks for Single Image Super-Resolution76
This paper proposes a unified approach called “multi-grained attention networks (MGAN)” which fully exploits the advantages of multi-scale and attention mechanisms in SR tasks and shows the superiority in terms of both accuracy and model size.
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- Palmprint Recognition across Different Devices62
A robust method to calculate the palm width is proposed, which can be effectively used for scale normalization of palmprints, and results show that orientation coding based methods achieved promising recognition performance for PRADD.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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