[CVPR 2019] f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
[paper] 作者提出了一個直推式特徵生成網絡。和f-CLSWGAN不同的是，論文沒有使用分類loss來學習更具有判別性的特徵。 1.VAE和WGAN信息互補，生成更魯棒的特徵； 2.使用判別器來學習未見類數據的流形 3.生成特徵可解釋
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相關文章
- 1. Paper Reading: f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
- 2. [CVPR 2018 論文筆記] Feature Generating Networks for Zero-Shot Learning
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- 4. 【KDD 2019】A Representation Learning Framework for Property Graphs
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- 7. 2019 CVPR: A main/subsidiary network framework for simplifying binary neural networks
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- 9. A Discriminative Feature Learning Approach for Deep Face Recognition
- 10. Learning a Discriminative Feature Network for Semantic Segmentation