- Allows the features to be determined separately from the classifier layer
- Only need to extract the features for the entire training set just once
- Enables us to train a convolutional network by using convolution

University of Virginia, Charlottesville

Three basic strategies for obtaining convolution kernels without supervised training:
- Initializing them randomly
- Setting each kernel to detect edges at a certain orientation or scale
- Item learning the kernels with an unsupervised criterion

Obtaining Kernels without Supervised Training

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

Learn Before

Related