Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics

© 2022 by the authors; CC BY-NC-ND license

head detection; YoloV4; NMS; soft-NMS; people counting; vehicle re-identification; license plate recognition; video surveillance; feature extraction; pedestrian detection; machine learning; end-to-end; anchor-free; feature reuse; correlation filters; second-order fitting; visual tracking; DCNN-BiLSTM; domain adaptation; MMD; fine-tuning; C-MAPSS; cross-working; small sample; blind image deblurring; image prior; sparse channel; sparsity; multi-output; kNN; metric learning; cost-weighted; geometric mean metric; motion deblurring; image super-resolution; multi-order attention; gated learning; decoupling; face recognition; second-order gradient; image gradient orientations; collaborative-representation-based classification; image aesthetic assessment; semi-supervised learning; label propagation; deep learning; computer vision; garbage quantity identification; YOLOX; NMS; Soft-NMS; stability; switched system; state-dependent switching; time delay; multi-source domain adaptation; Dempster–Shafer evidence theory; cross-domain classification; 3D reconstruction; multi-view stereo; structure from motion; background matting; adversarial example; feature transformation; black-box attack; ensemble attack; deep neural network; intelligent design; data analysis; models and algorithms; extension theory; scheme design; domain adaptation; adversarial learning; adversarial equilibrium; transferability quantification; power load forecasting; routing, modulation and spectrum assignment; elastic optical networks; deep reinforcement learning; knowledge distillation; aspect-based sentiment analysis; graph neural networks; dependency trees; dependency types; graph attention mechanism; syntactic; semantic; vehicle color recognition; low–high level joint task; object detection; joint semantic learning; deep neural network; rainy image recovery; XSS attack; traffic detection; payloads; fusion verification; hypergraph matching; similarity metric; information-theoretic metric learning; mixed noise removal; matrix nuclear norm; logarithm norm; ADMM; plug-and-play; aspect-level sentiment classification; external knowledge; KGE; GCN; discriminative feature learning; multidimensional scaling; fuzzy k-means; pairwise constraint propagation; iterative majorization algorithm; Aspect Level Sentiment Classification; Contrasitve Learning; Graph Convolutional Networks; aspect-based sentiment analysis; graph convolutional networks; commonsense knowledge graph; deep learning; anomaly detection; cyber–physical; industrial control systems; image classification; large-margin technique; deep neural network; robustness; anti-noise performance; cross-domain sentiment classification; word embedding; GAT; hate speech detection; contrastive learning; multi-task learning; deep reinforcement learning; attention mechanism; state reconstruction; gait adjustment; uncertain temporal knowledge graph; temporal knowledge graph; knowledge graph embedding; confidence score; n/a