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Sep, 2019
针对生成式零样本学习的特征混淆缓解
Alleviating Feature Confusion for Generative Zero-shot Learning
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Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang...
TL;DR
本文提出了一种新的特征生成网络AFC-GAN,用于解决基于GAN的零样本学习中存在的特征混淆问题,通过提出边界损失和特征混淆得分指标FCS等方法可以显著提高模型的性能。
Abstract
Lately,
generative adversarial networks
(GANs) have been successfully applied to
zero-shot learning
(ZSL) and achieved state-of-the-art performance. By synthesizing virtual unseen visual features, GAN-based metho
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