Self-supervised Learning with Local Attention-Aware Feature

Autor:	Pham, Trung X., Mina, Rusty John Lloyd, Issa, Dias, Yoo, Chang D.
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	In this work, we propose a novel methodology for self-supervised learning for generating global and local attention-aware visual features. Our approach is based on training a model to differentiate between specific image transformations of an input sample and the patched images. Utilizing this approach, the proposed method is able to outperform the previous best competitor by 1.03% on the Tiny-ImageNet dataset and by 2.32% on the STL-10 dataset. Furthermore, our approach outperforms the fully-supervised learning method on the STL-10 dataset. Experimental results and visualizations show the capability of successfully learning global and local attention-aware visual representations. Comment: 5 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.00475 Zobrazit plný text záznamu View this record from Arxiv