Weight Initialization Strategies in Neural Networks
Apply appropriate weight initialization methods to stabilize training and accelerate convergence in deep networks.
Proper weight initialization is crucial for deep network training. Initializing all weights to zero causes all neurons to learn identical features (symmetry problem). Random initialization from inappropriate distributions can cause activations to saturate (become very large or very small) or vanish. Xavier (Glorot) initialization scales weights based on layer input/output sizes to maintain activation variance across layers: uniform distribution in range [-√(6/(n_in+n_out)), √(6/(n_in+n_out))]. He initialization is better for ReLU activations: normal distribution with std = √(2/n_in). Proper initialization stabilizes training, improves convergence speed, and reduces gradient flow problems in deep networks.
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