R2FP: Rich and robust feature pooling for mining visual data

• Main Authors: XIONG, Wei, DU, Bo, ZHANG, Lefei, HU, Ruimin, BIAN, Wei, SHEN, Jialie, TAO, Dacheng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Autoencoder; Pooling; Representation learning; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/3539
• Institution: Singapore Management University

The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose a novel pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling (R2FP), to bette...