What is vanishing gradient?

As we add more and more hidden layers, backpropagation becomes less useful in passing information to the lower layers. In effect, as information is passed back, the gradients begin to vanish and become small relative to the weights of the network.

The vanishing gradient problem refers to the issue encountered in training deep neural networks where gradients become extremely small as they propagate backward through the network during the process of backpropagation. This phenomenon particularly affects networks with many layers, such as deep neural networks, recurrent neural networks (RNNs), and deep belief networks (DBNs).

When gradients become very small, it becomes difficult for the weights of the earlier layers in the network to update effectively, leading to slow or halted learning. As a result, these earlier layers may not learn meaningful representations of the data, hindering the overall performance of the network.

The vanishing gradient problem often occurs due to the use of activation functions that squash their input into a small range, such as the sigmoid or tanh functions, coupled with deeper networks. During backpropagation, the gradients calculated for these functions can become very small, approaching zero, as the derivative of the function approaches zero for large or small input values. Consequently, the gradients diminish exponentially as they propagate backward through the layers, hence the term “vanishing gradient.”

To mitigate the vanishing gradient problem, several techniques have been proposed, including:

1. Initialization techniques: Using proper weight initialization methods such as Xavier or He initialization can help alleviate the vanishing gradient problem.
2. Activation functions: ReLU (Rectified Linear Unit) and its variants like Leaky ReLU, ELU, and SELU are less prone to the vanishing gradient problem compared to sigmoid or tanh functions.
3. Batch normalization: Normalizing the inputs to each layer can stabilize training and reduce the likelihood of vanishing gradients.
4. Gradient clipping: Limiting the magnitude of gradients during training can prevent them from becoming too small or too large.
5. Skip connections: Architectural modifications like skip connections (e.g., in Residual Networks or Highway Networks) allow gradients to flow directly through the network, bypassing certain layers, thereby mitigating the vanishing gradient problem.

By employing these techniques, deep learning practitioners can address the vanishing gradient problem and train more effective and efficient deep neural networks.