How transferable are features in deep neural networks? 论文笔记
0 摘要 在自然图像上训练的深度神经网络都表现出一种奇怪的现象：模型第一层的参数和Gabor filters的参数非常相似（也就是说模型拥有经典图像算法中具有的优秀性质，Gabor filter是比较通用的滤波器，也就是说模型在一开始也要完成一些常规模式的计算过程，这一点和经典图像算法是一样的）。第一层的特征并不是特定于某一数据集或者某一任务，而是通用的特征，它们适用于许多数据集和普遍的任
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相关文章
- 1. How transferable are features in deep neural networks?
- 2. 论文笔记：How transferable are features in deep neural networks?
- 3. How transferable are features in deep nerual networks? 【paper review】
- 4. Learning Transferable Features with Deep Adaptation Networks
- 5. 翻译“Learning Transferable Features with Deep Adaptation Networks”
- 6. Understanding Neural Networks Through Deep Visualization 论文笔记
- 7. [论文笔记]Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
- 8. How powerful are graph neural networks?
- 9. HOW POWERFUL ARE GRAPH NEURAL NETWORKS?
- 10. Neural Networks and Deep Learning-引论