CVPR2020--ATSS： Adaptive Training Sample Selection
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection ATSS爲CVPR2020中的一篇論文，論文題目如上所示，大體意思爲通過自適應選擇訓練樣本來彌補基於錨和無錨檢測器的差距。因爲目前大多數目標檢測成果都是在anchor-based的基礎上產生的，
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- 1. AdaptIS: Adaptive Instance Selection Network
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- 3. 論文筆記：目標檢測正負樣本劃分方法Adaptive Training Sample Selection (ATSS)原理
- 4. 目標檢測論文: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample
- 5. 【論文筆記】HSIC WIth Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement
- 6. [目標檢測]Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample S
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相關文章
- 1. AdaptIS: Adaptive Instance Selection Network
- 2. Adaptive Reference Sample Smoothing(RSAF)
- 3. 論文筆記：目標檢測正負樣本劃分方法Adaptive Training Sample Selection (ATSS)原理
- 4. 目標檢測論文: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample
- 5. 【論文筆記】HSIC WIth Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement
- 6. [目標檢測]Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample S
- 7. Hyperspectral Band Selection via Adaptive Subspace Partition Strategy
- 8. [Converge] Feature Selection in training of Deep Learning
- 9. MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走讀
- 10. Machine Learning Course 4 Selection of Model