Metric-Based Attention Feature Learning for Video Action Recognition by Dae Ha Kim, Fazliddin Anvarov, Jun Min Lee, Byung Cheol Song

Get full text

A Sensor Image Dehazing Algorithm Based on Feature Learning by Kun Liu, Linyuan He, Shiping Ma, Shan Gao, Duyan Bi

Subjects: Get full text

Robust Unsupervised Multi-View Feature Learning With Dynamic Graph by Yikun Li, Li Liu, Dan Shi, Hui Cui, Xu Lu

Subjects: Get full text

Neural network feature learning based on image self-encoding by Yangyang Liu, Minghua Tian, Chang Xu, Lixiang Zhao

“…If the neural network feature learning expression is combined with the image retrieval field, it will definitely improve the application of image retrieval technology. …”
Get full text

Unsupervised Feature Learning for Speech Emotion Recognition Based on Autoencoder by Yangwei Ying, Yuanwu Tu, Hong Zhou

“…Currently, the most effective approach is to make use of unsupervised feature learning techniques to extract speech features from available speech data and generate emotion classifiers with these features. …”
Get full text

Consecutive multiscale feature learning-based image classification model by Bekhzod Olimov, Barathi Subramanian, Rakhmonov Akhrorjon Akhmadjon Ugli, Jea-Soo Kim, Jeonghong Kim

“…Specifically, we present a consecutive multiscale feature-learning network (CMSFL-Net) that employs a consecutive feature-learning approach based on the usage of various feature maps with different receptive fields to achieve faster training/inference and higher accuracy. …”
Get full text

Temporal Feature Learning and Pulse Prediction for Radars with Variable Parameters by Shuo Yuan, Zhang-Meng Liu

Get full text

Multi-Scale Feature Learning for Language Identification of Overlapped Speech by Zuhragvl Aysa, Mijit Ablimit, Askar Hamdulla

Get full text

Unsupervised Feature Learning With Winner-Takes-All Based STDP by Paul Ferré, Paul Ferré, Franck Mamalet, Simon J. Thorpe

“…We present a novel strategy for unsupervised feature learning in image applications inspired by the Spike-Timing-Dependent-Plasticity (STDP) biological learning rule. …”
Get full text

Effective Transfer Learning with Label-Based Discriminative Feature Learning by Gyunyeop Kim, Sangwoo Kang

Get full text

Individualized Interaural Feature Learning and Personalized Binaural Localization Model by Xiang Wu, Dumidu S. Talagala, Wen Zhang, Thushara D. Abhayapala

Subjects: Get full text

Deep Feature Learning for Intrinsic Signature Based Camera Discrimination by Chaity Banerjee, Tharun Kumar Doppalapudi, Eduardo Pasiliao, Tathagata Mukherjee

Subjects: Get full text

QoS prediction in intelligent edge computing based on feature learning by Hongxia Zhang, Dengyue Wang, Wei Zhang, Lizhuang Tan, Godfrey Kibalya, Peiying Zhang, Kostromitin Konstantin Igorevich

Subjects: Get full text

Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection by Qingguo Zhou, Binbin Yong, Qingquan Lv, Jun Shen, Xin Wang

Subjects: “…mass spectrometry feature learning…”
Get full text

Adversarial Multiscale Feature Learning Framework for Overlapping Chromosome Segmentation by Liye Mei, Yalan Yu, Hui Shen, Yueyun Weng, Yan Liu, Du Wang, Sheng Liu, Fuling Zhou, Cheng Lei

Subjects: Get full text

Genetic Programming for High-Level Feature Learning in Crop Classification by Miao Lu, Ying Bi, Bing Xue, Qiong Hu, Mengjie Zhang, Yanbing Wei, Peng Yang, Wenbin Wu

Subjects: Get full text

Dense Point Cloud Reconstruction by Shape and Pose Features Learning by YANG Yongzhao, ZHANG Yujin, ZHANG Lijun

Get full text

Multiple deep features learning for object retrieval in surveillance videos by Haiyun Guo, Jinqiao Wang, Hanqing Lu

Subjects: “…multiple deep features learning…”
Get full text

Deep Feature Learning with Manifold Embedding for Robust Image Retrieval by Xin Chen, Ying Li

Subjects: Get full text

Feature learning in deep classifiers through Intermediate Neural Collapse by Rangamani, Akshay, Lindegaard, Marius, Galanti, Tomer, Poggio, Tomaso

“…In this paper, we conduct an empirical study of the feature learning process in deep classifiers. Recent research has identified a training phenomenon called Neural Collapse (NC), in which the top-layer feature embeddings of samples from the same class tend to concentrate around their means, and the top layer’s weights align with those features. …”
Get full text