A machine learning engineer is training a deep multilayer perceptron for a complex regression task. After several epochs, they observe that a substantial number of neurons in the hidden layers consistently output zero for all inputs in the validation dataset, and the model's performance has plateaued. Which of the following is the most likely explanation for this phenomenon?
The network is experiencing exploding gradients, leading to unstable weight updates and producing NaN (Not a Number) outputs.
The model is severely overfitting to the training data, causing poor generalization to the validation set.
The model is experiencing the 'dying ReLU' problem, where neurons become inactive due to receiving inputs that consistently result in a negative weighted sum.
The network is suffering from the vanishing gradient problem, preventing the weights of earlier layers from being updated effectively.
The correct answer explains the 'dying ReLU' problem. This issue occurs when a neuron using the Rectified Linear Unit (ReLU) activation function consistently receives a negative value for its weighted input sum. The ReLU function, defined as f(x) = max(0, x), will output zero for any negative input. If a neuron is in this state, its gradient during backpropagation will also be zero, meaning its weights will not be updated. This neuron becomes 'stuck' or 'dead', no longer contributing to the learning process, which can cause the model's overall performance to stagnate.
Overfitting describes a model that learns the training data too well but fails to generalize; it does not specifically explain why many neurons would have a zero output.
The vanishing gradient problem is more characteristic of activation functions like sigmoid or tanh, where gradients become extremely small in deep layers; ReLU was introduced to help mitigate this issue for positive inputs.
Exploding gradients would result in numerically unstable, large weight updates and likely cause the model's loss to become NaN, not a consistent zero output from many neurons.
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