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@ -70,11 +70,18 @@ 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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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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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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$$
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Out_{\delta}(X, Y)_i =
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\begin{cases}
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\begin{cases}
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0.5 * (y - f(x))^2, \quad |y - f(x)| \leq \delta \\
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0.5 * (Y_i - X_i)^2,
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\delta * (|y - f(x)| - 0.5 * \delta), \quad otherwise
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\quad |Y_i - X_i| \leq \delta \\
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\delta * (|Y_i - X_i| - 0.5 * \delta),
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\quad otherwise
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\end{cases}
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\end{cases}
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$$
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In the above equation, $Out_\delta(X, Y)_i$, $X_i$ and $Y_i$ represent the ith
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element of Out, X and Y.
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)DOC");
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)DOC");
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}
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}
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