[CVPR 2019] Gradient Matching Generative Networks for Zero-Shot Learning

时间 2020-07-25

标签 cvpr gradient matching generative networks zero shot learning 繁體版

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用于零样本学习的梯度匹配生成网络做者提出了一种新的Loss，梯度匹配Loss，来训练生成网络。该Loss能够测量生成样本产生的梯度信号的质量。使用GM Loss能够迫使生成器产生的样本可以训练出精确的分类器。间接的保证了生成器生成的样本的丰富性。html 代码git 文章目录 1. 生成式ZSL 2. example-synthesis based ZSL方法的问题 3. GAN->WGAN->

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1. Matching Networks for One Shot Learning
2. 【One Shot】《Matching Networks for One Shot Learning》
3. Neural Networks: Learning: Gradient checking
4. 论文《Matching Networks for One Shot Learning》阅读
5. Matching Networks for One Shot Learning论文解读
6. Matching Networks for One Shot Learning论文分析
7. Matching Networks for one Shot Learning 阅读笔记
8. CVPR 2019 paper
9. 【文献阅读】Perceptual Generative Adversarial Networks for Small Object Detection –CVPR-2017
10. [CVPR 2019] Adaptive Confidence Smoothing for Generalized Zero-Shot Learning
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1. Matching Networks for One Shot Learning
2. 【One Shot】《Matching Networks for One Shot Learning》
3. Neural Networks: Learning: Gradient checking
4. 论文《Matching Networks for One Shot Learning》阅读
5. Matching Networks for One Shot Learning论文解读
6. Matching Networks for One Shot Learning论文分析
7. Matching Networks for one Shot Learning 阅读笔记
8. CVPR 2019 paper
9. 【文献阅读】Perceptual Generative Adversarial Networks for Small Object Detection –CVPR-2017
10. [CVPR 2019] Adaptive Confidence Smoothing for Generalized Zero-Shot Learning

>>更多相关文章<<