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@ -70,11 +70,15 @@ input value and Y as the target value. Huber loss can evaluate the fitness of
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X to Y. Different from MSE loss, Huber loss is more robust for outliers. The
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shape of X and Y are [batch_size, 1]. The equation is:
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L_{\delta}(y, f(x)) =
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$$
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Out_{\delta}(i, x, y) =
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\begin{cases}
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0.5 * (y - f(x))^2, \quad |y - f(x)| \leq \delta \\
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\delta * (|y - f(x)| - 0.5 * \delta), \quad otherwise
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0.5 * (Input(i, y) - Input(i, x))^2,
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\quad |Input(i, y) - Input(i, x)| \leq \delta \\
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\delta * (|Input(i, y) - Input(i, x)| - 0.5 * \delta),
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\quad otherwise
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\end{cases}
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$$
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)DOC");
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}
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