【InceptionnetV3】Rethinking the Inception Architecture for Computer Vision摘要
看keras和mxnet的模型庫中都是隻有InceptionnetV3的模型而沒有InceptionnetV4的模型，好奇其中原因，找來InceptionnetV3的paper讀一讀。 研究方向：1.增加網絡規模，適當的增加計算 2.分解卷積和積極正則化 通常設計原則：1.避免瓶頸，特別是網絡前段：特徵維度不能斷崖式下降
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相關文章
- 1. Rethinking the Inception Architecture for Computer Vision
- 2. Rethinking the Inception Architecture for Computer Vision-Inception-V3
- 3. GoogleNetV3 Rethinking the Inception Architecture for Computer Vision
- 4. Inception系列2_Rethinking the Inception Architecture for Computer Vision
- 5. Inception Architecture for Computer Vision
- 6. Inception V3 -- 《Rethinking the Inception Architecture for Computer Vision》論文筆記
- 7. 《Inception V3-Rethinking the Inception Architecture for Computer Vision》論文筆記
- 8. Inception v3:Rethinking the Inception Architecture for Computer Vision翻譯理解
- 9. Rethinking the inception architecture for computer vision的 paper 相關知識
- 10. Rethinking the Inception Architecture for Computer Vision(2015)論文綜述