What is a degradation problem in very deep neural networks?
If a “shallow” model is able to achieve an accuracy, then their deeper counterparts should at least have the same accuracy. But, when the model gets deeper, it becomes more and more difficult for the layers to propagate the information from shallow layers and the information is lost. This is called Degradation Problem.
Why do deep networks suffer from the vanishing gradient problem?
Vanishing gradients is a particular problem with recurrent neural networks as the update of the network involves unrolling the network for each input time step, in effect creating a very deep network that requires weight updates.
Why might deep networks suffer from overfitting?
Too little learning and the model will perform poorly on the training dataset and on new data. The model will underfit the problem. Too much learning and the model will perform well on the training dataset and poorly on new data, the model will overfit the problem.
What is degradation problem?
Abstract: A widely observed phenomenon in deep learning is the degradation problem: increasing the depth of a network leads to a decrease in performance on both test and training data.
Why is the degradation problem of deep neural networks expected?
The degradation problem has been observed while training deep neural networks. As we increase network depth, accuracy gets saturated (this is expected). Why is this expected? Because we expect a sufficiently deep neural network to model all the intricacies of our data well.
Are there any problems with a deep network?
One problem commonly cited by professionals is that with deep networks composed of many dozens of layers, accuracy can become saturated, and some degradation can occur. Some talk about a different problem called “vanishing gradient” in which the gradient fluctuations become too small to be immediately useful.
Can a neural network be stuck in gradient descent?
As we delve into the problem more deeply, we’ll learn that the opposite phenomenon can also occur: the early layers may be learning well, but later layers can become stuck. In fact, we’ll find that there’s an intrinsic instability associated to learning by gradient descent in deep, many-layer neural networks.
Why do different layers of a neural network get stuck?
When we look closely, we’ll discover that the different layers in our deep network are learning at vastly different speeds. In particular, when later layers in the network are learning well, early layers often get stuck during training, learning almost nothing at all. This stuckness isn’t simply due to bad luck.