The vanishing gradient problem occurs in deep neural networks when gradients computed during backpropagation become extremely small as they propagate backward through layers. This makes early layers (closer to the input) learn very slowly or stop updating entirely, limiting the network’s ability to train effectively. The issue arises because gradients are calculated using the chain rule: if the derivatives of activation functions or weight matrices are consistently small, their repeated multiplication across layers causes gradients to shrink exponentially. For example, if a network uses sigmoid activation functions (whose derivatives max out at 0.25), after just five layers, the gradient could be as small as (0.25^5 = 0.00098), rendering early layers nearly static.
The problem is most pronounced in networks with many layers or activation functions prone to small derivatives. For instance, traditional recurrent neural networks (RNNs) processing long sequences often suffer from vanishing gradients because the same weights are reused across time steps. This makes it difficult for the network to learn dependencies between distant events. A classic example is training an RNN to predict the next word in a sentence where context from earlier words is critical. If gradients vanish, the model cannot adjust early layers to capture that context, leading to poor performance. Similarly, in convolutional networks, deep architectures without skip connections might fail to propagate meaningful updates to initial filters, resulting in underutilized early layers.
To address vanishing gradients, modern architectures and techniques have been developed. Using activation functions like ReLU (Rectified Linear Unit), which has a derivative of 1 for positive inputs, helps maintain gradient magnitude. Residual connections (e.g., in ResNet) allow gradients to bypass layers via skip connections, preventing multiplicative decay. Initialization strategies like He or Xavier initialization scale weights to avoid saturating activation functions. Additionally, batch normalization standardizes layer outputs, reducing the likelihood of gradients collapsing. For RNNs, gated architectures like LSTMs or GRUs use multiplicative gates to regulate gradient flow, enabling longer-term memory retention. These solutions collectively enable deeper networks to train effectively by preserving gradient signals across layers.