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Semi-hard constraint augmentation of triplet learning to improve image corruption classification
When facing the challenge of image distribution shift and natural corruptions, most of data augmentation methods only consider the diversity of...
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Integrating information by Kullback–Leibler constraint for text classification
Text classification is an important assignment for various text-related downstream assignments, such as fake news detection, sentiment analysis, and...
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Relationship constraint deep metric learning
AbstractDeep metric learning (DML) models aim to learn semantically meaningful representations in which similar samples are pulled together and...
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Feature constraint reinforcement based age estimation
As one of the critical biological characteristics of human age, the face has been widely studied for age prediction, which has broad application...
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Interactive and discriminative analysis dictionary learning for image classification
Dictionary learning is widely utilized in pattern recognition, and analysis dictionary learning is a prevalent image classification method. However,...
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Adaptive kernel selection network with attention constraint for surgical instrument classification
Computer vision (CV) technologies are assisting the health care industry in many respects, i.e., disease diagnosis. However, as a pivotal procedure...
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Dual-stream network with cross-layer attention and similarity constraint for micro-expression recognition
Micro-expression recognition (MER) is a pivotal research area within human emotion analysis. However, the fleeting, subtle, and complex nature of...
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An adversarial training-based mutual information constraint method
As an auxiliary loss function, the mutual information constraint is widely used in various deep learning tasks, such as deep reinforcement learning...
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A representation learning model based on stochastic perturbation and homophily constraint
The network representation learning task of fusing node multi-dimensional classification information aims to effectively combine node...
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Constraint-free discretized manifold-based path planner
Autonomous robotic path planning in partially known environments, such as warehouse robotics, deals with static and dynamic constraints. Static...
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mcVAE: disentangling by mean constraint
Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are...
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Multi-task learning model for citation intent classification in scientific publications
Citations play a significant role in the evaluation of scientific literature and researchers. Citation intent analysis is essential for academic...
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Algebraic Global Gadgetry for Surjective Constraint Satisfaction
The constraint satisfaction problem (CSP) on a finite relational structure B is to decide, given a set of constraints on variables where the...
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Double-constrained structured discriminant analysis-synthesis dictionary pair learning for pattern classification
Existing discriminant analysis-synthesis dictionary pair learning (ASDPL) methods learn a structured analysis dictionary containing multiple...
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Relative order constraint for monocular depth estimation
Monocular depth estimation, which is playing an increasingly important role in 3D scene understanding, has been attracting increasing attention in...
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A subspace constraint based approach for fast hierarchical graph embedding
Hierarchy network, as a type of complex graphs, is widely used in many application scenarios such as social network analysis in web, human resource...
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Relaxed support vector based dictionary learning for image classification
Discriminative dictionary learning (DDL) has attracted significant attention in the field of image classification. To enhance the classification...
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Graph Contrastive Learning Method with Sample Disparity Constraint and Feature Structure Graph for Node Classification
Most of the existing graph contrastive learning methods for node classification focus on exploiting topological information of the attributed...
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Scheduling IDK classifiers with arbitrary dependences to minimize the expected time to successful classification
This paper introduces and evaluates a general construct for trading off accuracy and overall execution duration in classification-based machine...
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Self-eliminating Discriminant Analysis Dictionary Learning for Pattern Classification
As a branch of dictionary learning (DL), analysis dictionary learning has been widely used for pattern classification, which achieves outstanding...
