Low-Shot Learning from Imaginary Data論文簡要解讀
Low-Shot Learning from Imaginary Data 論文摘要 論文要點 end-to-end訓練 Learned Hallucination Implementation details 最終效果 疑問點 論文摘要 本文主要提出了通過生成模型生成虛擬數據來擴充樣本的多樣性，並結合當前比較先進的元學習方法，通過end-to-end 訓練兩生成模型和分類算法，從而實現更好的lo
相關文章
- 1. 論文閱讀筆記《Low-Shot Learning from Imaginary Data》
- 2. 論文閱讀：AutoAugment: Learning Augmentation Strategies from Data
- 3. 論文簡讀—Imitation learning for language generation from unaligned data
- 4. [NLP論文閱讀] Supervised Learning of Universal Sentence Representations from NLI data
- 5. 【AutoAugment】2019-CVPR-AutoAugment: Learning Augmentation Strategies from Data-論文閱讀
- 6. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data 論文閱讀筆記
- 7. 論文理解《Learning from Synthetic Data for Crowd Counting in the Wild》
- 8. DSOD： Learning Deeply Supervised Object Detectors from Scratch 論文解讀
- 9. 《learning discriminative features from electroence》論文精讀只有摘要、結果和結論部分
- 10. 【論文筆記】Learning from Synthetic Data for Crowd Counting in the Wild
- 更多相關文章...
- • C# 文本文件的讀寫 - C#教程
- • *.hbm.xml映射文件詳解 - Hibernate教程
- • JDK13 GA發佈：5大特性解讀
- • Scala 中文亂碼解決
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 論文閱讀筆記《Low-Shot Learning from Imaginary Data》
- 2. 論文閱讀：AutoAugment: Learning Augmentation Strategies from Data
- 3. 論文簡讀—Imitation learning for language generation from unaligned data
- 4. [NLP論文閱讀] Supervised Learning of Universal Sentence Representations from NLI data
- 5. 【AutoAugment】2019-CVPR-AutoAugment: Learning Augmentation Strategies from Data-論文閱讀
- 6. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data 論文閱讀筆記
- 7. 論文理解《Learning from Synthetic Data for Crowd Counting in the Wild》
- 8. DSOD： Learning Deeply Supervised Object Detectors from Scratch 論文解讀
- 9. 《learning discriminative features from electroence》論文精讀只有摘要、結果和結論部分
- 10. 【論文筆記】Learning from Synthetic Data for Crowd Counting in the Wild