Unsupervised discriminative feature representation via adversarial auto-encoder
Sign Up to like & getrecommendations! Published in 2019 at "Applied Intelligence"
DOI: 10.1007/s10489-019-01581-7
Abstract: Feature representation is generally applied to reducing the dimensions of high-dimensional data to accelerate the process of data handling and enhance the performance of pattern recognition. However, the dimensionality of data nowadays appears to be… read more here.
Keywords: auto encoder; feature representation; adversarial auto; feature ... See more keywords
A strong feature representation for siamese network tracker
Sign Up to like & getrecommendations! Published in 2020 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-020-09164-2
Abstract: Because AlexNet is too shallow to form a strong feature representation, the trackers based on the Siamese network have an accuracy gap comparing with state-of-the-art algorithms. Both deep features and appearance features benefit tracking accuracy.… read more here.
Keywords: siamese network; strong feature; feature representation; network ... See more keywords
Logistic regression projection-based feature representation for visual domain adaptation
Sign Up to like & getrecommendations! Published in 2020 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-020-01649-9
Abstract: The performance of visual image recognizers is considerably degraded while the training and test image sets not to follow the same distribution. In this study, we propose a novel method for unsupervised domain adaptation, called… read more here.
Keywords: domain adaptation; feature representation;
Cross-covariance regularized autoencoders for nonredundant sparse feature representation
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.07.050
Abstract: Abstract We propose a new feature representation algorithm using cross-covariance in the context of deep learning. Existing feature representation algorithms based on the sparse autoencoder and nonnegativity-constrained autoencoder tend to produce duplicative encoding and decoding… read more here.
Keywords: cross covariance; feature representation; feature;
GenELM: Generative Extreme Learning Machine feature representation
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.05.098
Abstract: Abstract Extreme Learning Machine (ELM) feature representation has been drawing increasing attention, and most of the previous works devoted to learning discriminative features. However, we argue that such kind of features suffer from “categories bias”… read more here.
Keywords: feature representation; learning machine; feature; extreme learning ... See more keywords
Multi-scale and multi-branch feature representation for person re-identification
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.074
Abstract: Abstract Multi-scale feature fusion has been proven effective in substantial person re-identification (ReID) works. However, the existing multi-scale feature fusion is based on features of different semantic levels. We propose a novel multi-scale and multi-branch… read more here.
Keywords: person; feature representation; multi scale; feature ... See more keywords
A novel feature representation method based on original waveforms for acoustic emission signals
Sign Up to like & getrecommendations! Published in 2020 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2019.106365
Abstract: Abstract One of the most important issues arising in the use of acoustic emission (AE) for nondestructive process monitoring is the accurate identification of potential process malfunctions to avoid premature failure. In some cases, the… read more here.
Keywords: acoustic emission; method; feature representation; representation method ... See more keywords
Iterative feature representation algorithm to improve the predictive performance of N7-methylguanosine sites
Sign Up to like & getrecommendations! Published in 2021 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbaa278
Abstract: MOTIVATION N7-methylguanosine (m7G) is an important epigenetic modification, playing an essential role in gene expression regulation. Therefore, accurate identification of m7G modifications will facilitate revealing and in-depth understanding their potential functional mechanisms. Although high-throughput experimental… read more here.
Keywords: representation algorithm; feature; feature representation; iterative feature ... See more keywords
CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning
Sign Up to like & getrecommendations! Published in 2020 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bby091
Abstract: Cell-penetrating peptides (CPPs) have been shown to be a transport vehicle for delivering cargoes into live cells, offering great potential as future therapeutics. It is essential to identify CPPs for better understanding of their functional… read more here.
Keywords: large scale; cpps; feature representation; scale identification ... See more keywords
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Sign Up to like & getrecommendations! Published in 2020 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btaa160
Abstract: MOTIVATION Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of peptides as drug candidates. The accurate prediction of hemolytic peptides (HLPs) and its… read more here.
Keywords: hemolytic peptide; hlppred fuse; feature representation; prediction hemolytic ... See more keywords
Military Vehicle Object Detection Based on Hierarchical Feature Representation and Refined Localization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3207153
Abstract: Military vehicle object detection technology in complex environments is the basis for the implementation of reconnaissance and tracking tasks for weapons and equipment, and is of great significance for information and intelligent combat. In response… read more here.
Keywords: feature representation; detection; military vehicle;