Howtosignalprocessing
- What is an end to end training?
- What is end to end training of CNN?
- Why is end to end training important?
- How do you determine when to stop training a neural network?
What is an end to end training?
End-to-end (E2E) learning refers to training a possibly complex learning system represented by a single model (specifically a Deep Neural Network) that represents the complete target system, bypassing the intermediate layers usually present in traditional pipeline designs.
What is end to end training of CNN?
This procedure trains the two components in two completely separate phases. Treat the whole architecture as a single network and backpropagete the gradients to the CNN so that it also can be trained. This procedure trains the two components simultaneously. This is what we call end-to-end training.
Why is end to end training important?
In all such examples, the idea is to let the network go from the "raw-est" possible data to the final-most output. This is found to perform better. End-to-end learning reduces the effort of human design and performs better in most applications.
How do you determine when to stop training a neural network?
Stop Training When Generalization Error Increases
During training, the model is evaluated on a holdout validation dataset after each epoch. If the performance of the model on the validation dataset starts to degrade (e.g. loss begins to increase or accuracy begins to decrease), then the training process is stopped.
