DCNNs Trained by Cascaded Softmax and Generalized Large-Margin Losses
利用級聯Softmax和廣義大邊緣損失訓練的改進DCNNs進行細粒度圖像分類 Abstract: 我們使用一般的深度卷積神經網絡(DCNN)來開發一個細粒度的圖像分類器。我們從以下兩個方面來提高DCNN模型的細粒度圖像分類精度。首先，對給定訓練數據集中包含的細粒度圖像類的h級層次標籤結構進行更好的建模，我們引入h全連通(fc)層來代替給定DCNN模型的頂層fc層，並用級聯的softmax損失訓練它
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相關文章
- 1. [Scikit-learn] 1.1 Generalized Linear Models - Logistic regression & Softmax
- 2. [Scikit-learn] 1.5 Generalized Linear Models - SGD for Classification
- 3. LR and Softmax
- 4. Generalized face anti-spoofing by detecting pulse from face videos
- 5. The GAN Landscape: Losses, Architectures, Regularization, and Normalization
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- 7. 論文《Perceptual Losses for Real-Time Style Transfer and Super-Resolution》
- 8. Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 9. 論文筆記1.2——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 10. 【CV基石】Softmax and CrossEntropy