KL divergence (Kullback–Leibler) - D K L ( p | q ) = ∫ x p ( x ) log p ( x ) q ( x ) d x . D K L is zero when p ( x ) is equal to q ( x ) , JS Divergence (Jensen–Shannon) - D J S ( p | q ) = 1 2 D K L ( p | p + q 2 ) + 1 ...
JS divergence is bounded by 0 and 1, and, unlike KL divergence, is symmetric and smoother. Significant success in GAN training was achieved when the loss was switched from KL to JS divergence. WGAN uses Wasserstein distance, $W(p_r, p_g) = \frac{1}{K} \sup_{| f |L \leq K} ...
It's never negative, and it's 0 only when y and ˆy are the same. Note that minimizing cross entropy is the same as minimizing the KL divergence from ˆy to y. What does cross-entropy loss do? Cross-entropy loss, or log loss,measures the performance of a classification model whos...
No matter which form it is, both types cause NMD to occur more often in only one of the two species than in both species, and can hence explain to a considerable extent NMD status divergence. Where an orthologous exon can be found, the nonsense-mediated decay-specific exon is or was ...
The Kullback-Leibler divergence, or relative entropy [7], is a measure of the difference between two probability density func- tions P and Q. It is not a distance, as it is non-commutative and does not satisfy the triangle inequality. The KL divergence of Q from P, where P and Q are...
Compared to KL and JS divergences, Wasserstein metric gives a smooth measure (without sudden jumps in divergence). This makes it much more suitable for creating a stable learning process during the gradient descent. Also, compared to KL and JS, Wasserstein distance is differentiable nearly ...
KL divergence (Kullback–Leibler) - $D_{KL}(p | q) = \int_x p(x) \log \frac{p(x)}{q(x)} dx$. $D_{KL}$ is zero when $p(x)$ is equal to $q(x)$, JS Divergence (Jensen–Shannon) - $D_{JS}(p | q) = \frac{1}{2} D_{KL}(p | \frac{p + q}{2}) + \...
Compared to KL and JS divergences, Wasserstein metric gives a smooth measure (without sudden jumps in divergence). This makes it much more suitable for creating a stable learning process during the gradient descent. Also, compared to KL and JS, Wasserstein distance is differentiable nearly ...
KL divergence (Kullback–Leibler) - $D_{KL}(p | q) = \int_x p(x) \log \frac{p(x)}{q(x)} dx$. $D_{KL}$ is zero when $p(x)$ is equal to $q(x)$, JS Divergence (Jensen–Shannon) - $D_{JS}(p | q) = \frac{1}{2} D_{KL}(p | \frac{p + q}{2}) + \...
JS divergence is bounded by 0 and 1, and, unlike KL divergence, is symmetric and smoother. Significant success in GAN training was achieved when the loss was switched from KL to JS divergence. WGAN uses Wasserstein distance, $W(p_r, p_g) = \frac{1}{K} \sup_{| f |L \leq K} ...