論文閱讀筆記《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
核心思想 本文是在MAML的基礎上進一步探索利用元學習實現無模型限制的小樣本學習算法。思路與MAML和Meta-LSTM比較接近，首先MAML是利用元學習的方式獲得一個較好的初始化參數，在此基礎上只需要進行少量樣本的微調訓練就可以得到較好的結果，這種方式實現簡單，但由於只對初始化參數進行學習，模型的容量有限。Meta-LSTM則是利用LSTM網絡作爲外層網絡對內層網絡的各項優化參數（學習率、衰
相關文章
- 1. 論文閱讀筆記《Deep Meta-Learning: Learning to Learn in the Concept Space》
- 2. Machine Learning & Deep Learning 論文閱讀筆記
- 3. 論文閱讀筆記《Learning to Compare: Relation Network for Few-Shot Learning》
- 4. Meta-SGD: Learning to learn quickly for few shot learning---翻譯理解
- 5. 論文閱讀筆記《Learning Embedding Adaptation for Few-Shot Learning》
- 6. 論文閱讀筆記 Meta-Learning for Batch Mode Active Learning
- 7. 論文閱讀筆記《Meta-Learning of Neural Architectures for Few-Shot Learning》
- 8. 論文閱讀筆記《Task-Agnostic Meta-Learning for Few-shot Learning》
- 9. 論文閱讀筆記 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 10. 論文閱讀筆記：Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
- 更多相關文章...
- • RSS 閱讀器 - RSS 教程
- • PHP 實例 - AJAX RSS 閱讀器 - PHP教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • JDK13 GA發佈：5大特性解讀
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。
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《Deep Meta-Learning: Learning to Learn in the Concept Space》
- 2. Machine Learning & Deep Learning 論文閱讀筆記
- 3. 論文閱讀筆記《Learning to Compare: Relation Network for Few-Shot Learning》
- 4. Meta-SGD: Learning to learn quickly for few shot learning---翻譯理解
- 5. 論文閱讀筆記《Learning Embedding Adaptation for Few-Shot Learning》
- 6. 論文閱讀筆記 Meta-Learning for Batch Mode Active Learning
- 7. 論文閱讀筆記《Meta-Learning of Neural Architectures for Few-Shot Learning》
- 8. 論文閱讀筆記《Task-Agnostic Meta-Learning for Few-shot Learning》
- 9. 論文閱讀筆記 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 10. 論文閱讀筆記：Learning Attribute-to-Feature Mappings for Cold-Start Recommendations