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symmetry breaking

Giving every neuron in a layer a different starting weight, so gradient descent can make them specialize instead of updating them identically forever.

Examples

  • Two neurons that both start at weights (0,0) receive the identical gradient and land at the identical (-0.12,-0.06) after one update -- symmetry never breaks.

See also

Xavier initialization He initialization
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