Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

Mang Ye, Xu Zhang, Pong C. Yuen, Shih-Fu Chang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6210-6219

Abstract


This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories.

Related Material


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[bibtex]
@InProceedings{Ye_2019_CVPR,
author = {Ye, Mang and Zhang, Xu and Yuen, Pong C. and Chang, Shih-Fu},
title = {Unsupervised Embedding Learning via Invariant and Spreading Instance Feature},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}