Prevention of overfitting in convolutional layers of a CNN

Question

I\'m using TensorFlow to train a Convolutional Neural Network (CNN) for a sign language application. The CNN has to classify 27 different labels, so unsurprisingly, a major

Answer 1

How can I fight overfitting?

• Get more data (or data augmentation)
• Dropout (see paper, explanation, dropout for cnns)
• DropConnect
• Regularization (see my masters thesis, page 85 for examples)
• Feature scale clipping
• Global average pooling
• Make network smaller
• Early stopping

How can I improve my CNN?

Thoma, Martin. "Analysis and Optimization of Convolutional Neural Network Architectures." arXiv preprint arXiv:1707.09725 (2017).

See chapter 2.5 for analysis techniques. As written in the beginning of that chapter, you can usually do the following:

• (I1) Change the problem definition (e.g., the classes which are to be distinguished)
• (I2) Get more training data
• (I3) Clean the training data
• (I4) Change the preprocessing (see Appendix B.1)
• (I5) Augment the training data set (see Appendix B.2)
• (I6) Change the training setup (see Appendices B.3 to B.5)
• (I7) Change the model (see Appendices B.6 and B.7)

Misc

The CNN has to classify 27 different labels, so unsurprisingly, a major problem has been addressing overfitting.

I don't understand how this is connected. You can have hundreds of labels without a problem of overfitting.