論文：Baby Steps Towards Few-Shot Learning with Multiple Semantics
1、Baby Steps Towards Few-Shot Learning with Multiple Semantics 目的：記錄下對文章的理解 摘要：額外的語義信息可以顯著提高少樣本學習的能力，論文在少樣本學習中結合了多種豐富的語義，這裏語義信息包括類別標籤、屬性和自然語言描述等信息。論文在miniImageNet 和CUB few-shot benchmark做了豐富實驗。下面幾個問題可
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相關文章
- 1. 論文閱讀筆記《Baby Steps Towards Few-Shot Learning with Multiple Semantics》
- 2. Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics 之論文詳解
- 3. 論文筆記：Towards perspective-free object counting with deep learning
- 4. 論文-《Towards Optimal Binary Code Learning via Ordinal Embedding》
- 5. [論文解讀]Towards Improved Testing For Deep Learning
- 6. (MIL)Robust Object Tracking with Online Multiple Instance Learning論文學習筆記
- 7. [論文閱讀]Learning Discriminative Features with Multiple Granularityfor Person Re-Identification
- 8. Visual Tracking with Online Multiple Instance Learning (MIL)目標跟蹤論文筆記
- 9. 論文略讀 | Learning End-to-End Goal-Oriented Dialog with Multiple Answers
- 10. Google's Machine Learning Crash Course #04# First Steps with TensorFlow