Amongst deep studying practitioners, Kullback-Leibler divergence (KL divergence) is probably greatest identified for its position in coaching variational autoencoders (VAEs). To be taught an informative latent house, we don’t simply optimize for good reconstruction. Reasonably, we additionally impose a previous on the latent distribution, and intention to maintain them shut – typically, by minimizing KL divergence.

On this position, KL divergence acts like a watchdog; it’s a constraining, regularizing issue, and if anthropomorphized, would appear stern and extreme. If we depart it at that, nonetheless, we’ve seen only one aspect of its character, and are lacking out on its complement, an image of playfulness, journey, and curiosity. On this submit, we’ll check out that different aspect.

Whereas being impressed by a sequence of tweets by Simon de Deo, enumerating functions of KL divergence in an unlimited variety of disciplines,

we don’t aspire to supply a complete write-up right here – as talked about within the preliminary tweet, the subject might simply fill an entire semester of examine.

The far more modest targets of this submit, then, are

1. to rapidly recap the position of KL divergence in coaching VAEs, and point out similar-in-character functions;
2. as an example that extra playful, adventurous “different aspect” of its character; and
3. in a not-so-entertaining, however – hopefully – helpful method, differentiate KL divergence from associated ideas comparable to cross entropy, mutual info, or free power.

Earlier than although, we begin with a definition and a few terminology.

KL divergence in a nutshell

KL divergence is the anticipated worth of the logarithmic distinction in chances in accordance with two distributions, (p) and (q). Right here it’s in its discrete-probabilities variant:

[begin{equation}
D_{KL}(p||q) = sumlimits_{x} p(x) log(frac{p(x)}{q(x)})
tag{1}
end{equation}]

Notably, it’s uneven; that’s, (D_{KL}(p||q)) just isn’t the identical as (D_{KL}(q||p)). (Which is why it’s a divergence, not a distance.) This side will play an vital position in part 2 devoted to the “different aspect.”

To emphasize this asymmetry, KL divergence is typically known as relative info (as in “info of (p) relative to (q)”), or info acquire. We agree with one in every of our sources that due to its universality and significance, KL divergence would in all probability have deserved a extra informative identify; comparable to, exactly, info acquire. (Which is much less ambiguous pronunciation-wise, as properly.)

KL divergence, “villain”

In lots of machine studying algorithms, KL divergence seems within the context of variational inference. Usually, for lifelike information, precise computation of the posterior distribution is infeasible. Thus, some type of approximation is required. In variational inference, the true posterior (p^*) is approximated by a less complicated distribution, (q), from some tractable household.
To make sure we have now an excellent approximation, we decrease – in principle, at the least – the KL divergence of (q) relative to (p^*), thus changing inference by optimization.

In observe, once more for causes of intractability, the KL divergence minimized is that of (q) relative to an unnormalized distribution (widetilde{p})

[begin{equation}
J(q) = D_{KL}(q||widetilde{p})
tag{2}
end{equation}]

the place (widetilde{p}) is the joint distribution of parameters and information:

[begin{equation}
widetilde{p}(mathbf{x}) = p(mathbf{x}, mathcal{D}) = p^*(mathbf{x}) p(mathcal{D})
tag{3}
end{equation}]

and (p^*) is the true posterior:

[begin{equation}
p^*(mathbf{x}) = p(mathbf{x}|mathcal{D})
tag{4}
end{equation}]

Equal to that formulation (eq. (2)) – for a derivation see (Murphy 2012) – is that this, which exhibits the optimization goal to be an higher certain on the detrimental log-likelihood (NLL):

[begin{equation}
J(q) = D_{KL}(q||p^*) – log p(D)
tag{5}
end{equation}]

Yet one more formulation – once more, see (Murphy 2012) for particulars – is the one we truly use when coaching (e.g.) VAEs. This one corresponds to the anticipated NLL plus the KL divergence between the approximation (q) and the imposed prior (p):

[begin{equation}
J(q) = D_{KL}(q||p) – E_q[- log p(mathcal{D}|mathbf{x})]
tag{6}
finish{equation}]

Negated, this formulation can also be known as the ELBO, for proof decrease certain. Within the VAE submit cited above, the ELBO was written

[begin{equation}
ELBO = E[log p(x|z)] – KL(q(z)||p(z))
tag{7}
finish{equation}]

with (z) denoting the latent variables ((q(z)) being the approximation, (p(z)) the prior, typically a multivariate regular).

Past VAEs

Generalizing this “conservative” motion sample of KL divergence past VAEs, we are able to say that it expresses the standard of approximations. An vital space the place approximation takes place is (lossy) compression. KL divergence supplies a approach to quantify how a lot info is misplaced after we compress information.

Summing up, in these and comparable functions, KL divergence is “dangerous” – though we don’t need it to be zero (or else, why hassle utilizing the algorithm?), we actually need to preserve it low. So now, let’s see the opposite aspect.

KL divergence, good man

In a second class of functions, KL divergence just isn’t one thing to be minimized. In these domains, KL divergence is indicative of shock, disagreement, exploratory habits, or studying: This actually is the angle of info acquire.

Shock

One area the place shock, not info per se, governs habits is notion. For instance, eyetracking research (e.g., (Itti and Baldi 2005)) confirmed that shock, as measured by KL divergence, was a greater predictor of visible consideration than info, measured by entropy. Whereas these research appear to have popularized the expression “Bayesian shock,” this compound is – I feel – not essentially the most informative one, as neither half provides a lot info to the opposite. In Bayesian updating, the magnitude of the distinction between prior and posterior displays the diploma of shock led to by the information – shock is an integral a part of the idea.

Thus, with KL divergence linked to shock, and shock rooted within the elementary strategy of Bayesian updating, a course of that could possibly be used to explain the course of life itself, KL divergence itself turns into elementary. We might get tempted to see it in all places. Accordingly, it has been utilized in many fields to quantify unidirectional divergence.

For instance, (Zanardo 2017) have utilized it in buying and selling, measuring how a lot an individual disagrees with the market perception. Increased disagreement then corresponds to greater anticipated good points from betting towards the market.

Nearer to the realm of deep studying, it’s utilized in intrinsically motivated reinforcement studying (e.g., (Solar, Gomez, and Schmidhuber 2011)), the place an optimum coverage ought to maximize the long-term info acquire. That is attainable as a result of like entropy, KL divergence is additive.

Though its asymmetry is related whether or not you utilize KL divergence for regularization (part 1) or shock (this part), it turns into particularly evident when used for studying and shock.

Asymmetry in motion

Wanting once more on the KL components

[begin{equation}
D_{KL}(p||q) = sumlimits_{x} p(x) log(frac{p(x)}{q(x)})
tag{1}
end{equation}]

the roles of (p) and (q) are essentially totally different. For one, the expectation is computed over the primary distribution ((p) in (1)). This side is vital as a result of the “order” (the respective roles) of (p) and (q) might must be chosen in accordance with tractability (which distribution can we common over).

Secondly, the fraction contained in the (log) implies that if (q) is ever zero at some extent the place (p) isn’t, the KL divergence will “blow up.” What this implies for distribution estimation basically is properly detailed in Murphy (2012). Within the context of shock, it implies that if I be taught one thing I used to suppose had likelihood zero, I shall be “infinitely stunned.”

To keep away from infinite shock, we are able to be certain that our prior likelihood is rarely zero. However even then, the fascinating factor is that how a lot info we acquire in anyone occasion relies on how a lot info I had earlier than. Let’s see a easy instance.

Assume that in my present understanding of the world, black swans in all probability don’t exist, however they may … perhaps 1 % of them is black. Put in another way, my prior perception of a swan, ought to I encounter one, being black is (q = 0.01).

Now in reality I do encounter one, and it’s black.
The knowledge I’ve gained is:

[begin{equation}
l(p,q) = 0 * log(frac{0}{0.99}) + 1 * log(frac{1}{0.01}) = 6.6 bits
tag{8}
end{equation}]

Conversely, suppose I’d been far more undecided earlier than; say I’d have thought the percentages had been 50:50.
On seeing a black swan, I get loads much less info:

[begin{equation}
l(p,q) = 0 * log(frac{0}{0.5}) + 1 * log(frac{1}{0.5}) = 1 bit
tag{9}
end{equation}]

This view of KL divergence, when it comes to shock and studying, is inspiring – it could lead on one to seeing it in motion in all places. Nonetheless, we nonetheless have the third and ultimate process to deal with: rapidly evaluate KL divergence to different ideas within the space.

Entropy

All of it begins with entropy, or uncertainty, or info, as formulated by Claude Shannon.
Entropy is the common log likelihood of a distribution:

[begin{equation}
H(X) = – sumlimits_{x=1}^n p(x_i) log(p(x_i))
tag{10}
end{equation}]

As properly described in (DeDeo 2016), this formulation was chosen to fulfill 4 standards, one in every of which is what we generally image as its “essence,” and one in every of which is particularly fascinating.

As to the previous, if there are (n) attainable states, entropy is maximal when all states are equiprobable. E.g., for a coin flip uncertainty is highest when coin bias is 0.5.

The latter has to do with coarse-graining, a change in “decision” of the state house. Say we have now 16 attainable states, however we don’t actually care at that stage of element. We do care about 3 particular person states, however all the remainder are mainly the identical to us. Then entropy decomposes additively; complete (fine-grained) entropy is the entropy of the coarse-grained house, plus the entropy of the “lumped-together” group, weighted by their chances.

Subjectively, entropy displays our uncertainty whether or not an occasion will occur. Curiously although, it exists within the bodily world as properly: For instance, when ice melts, it turns into extra unsure the place particular person particles are. As reported by (DeDeo 2016), the variety of bits launched when one gram of ice melts is about 100 billion terabytes!

As fascinating as it’s, info per se might, in lots of instances, not be the very best technique of characterizing human habits. Going again to the eyetracking instance, it’s fully intuitive that individuals have a look at shocking components of photos, not at white noise areas, that are the utmost you possibly can get when it comes to entropy.

As a deep studying practitioner, you’ve in all probability been ready for the purpose at which we’d point out cross entropy – essentially the most generally used loss operate in categorization.

Cross entropy

The cross entropy between distributions (p) and (q) is the entropy of (p) plus the KL divergence of (p) relative to (q). In case you’ve ever carried out your personal classification community, you in all probability acknowledge the sum on the very proper:

[begin{equation}
H(p,q) = H(p) + D_{KL}(p||q) = – sum p log(q)
tag{11}
end{equation}]

In info theory-speak, (H(p,q)) is the anticipated message size per datum when (q) is assumed however (p) is true.
Nearer to the world of machine studying, for mounted (p), minimizing cross entropy is equal to minimizing KL divergence.

Mutual info

One other extraordinarily vital amount, utilized in many contexts and functions, is mutual info. Once more citing DeDeo, “you’ll be able to consider it as essentially the most normal type of correlation coefficient you could measure.”

With two variables (X) and (Y), we are able to ask: How a lot can we find out about (X) after we find out about a person (y), (Y=y)? Averaged over all (y), that is the conditional entropy:

[begin{equation}
H(X|Y) = – sumlimits_{i} P(y_i) log(H(X|y_i))
tag{12}
end{equation}]

Now mutual info is entropy minus conditional entropy:

[begin{equation}
I(X, Y) = H(X) – H(X|Y) = H(Y) – H(Y|X)
tag{13}
end{equation}]

This amount – as required for a measure representing one thing like correlation – is symmetric: If two variables (X) and (Y) are associated, the quantity of knowledge (X) offers you about (Y) is the same as that (Y) offers you about (X).

KL divergence is a part of a household of divergences, known as f-divergences, used to measure directed distinction between likelihood distributions. Let’s additionally rapidly look one other information-theoretic measure that in contrast to these, is a distance.

Jensen-Shannon distance

In math, a distance, or metric, in addition to being non-negative has to fulfill two different standards: It should be symmetric, and it should obey the triangle inequality.

Each standards are met by the Jensen-Shannon distance. With (m) a combination distribution:

[begin{equation}
m_i = frac{1}{2}(p_i + q_i)
tag{14}
end{equation}]

the Jensen-Shannon distance is a median of KL divergences, one in every of (m) relative to (p), the opposite of (m) relative to (q):

[begin{equation}
JSD = frac{1}{2}(KL(m||p) + KL(m||q))
tag{15}
end{equation}]

This is able to be an excellent candidate to make use of had been we excited by (undirected) distance between, not directed shock brought on by, distributions.

Lastly, let’s wrap up with a final time period, limiting ourselves to a fast glimpse at one thing complete books could possibly be written about.

(Variational) Free Power

Studying papers on variational inference, you’re fairly prone to hear folks speaking not “simply” about KL divergence and/or the ELBO (which as quickly as you recognize what it stands for, is simply what it’s), but in addition, one thing mysteriously known as free power (or: variational free power, in that context).

For sensible functions, it suffices to know that variational free power is detrimental the ELBO, that’s, corresponds to equation (2). However for these , there’s free power as a central idea in thermodynamics.

On this submit, we’re primarily excited by how ideas are associated to KL divergence, and for this, we observe the characterization John Baez offers in his aforementioned discuss.

Free power, that’s, power in helpful type, is the anticipated power minus temperature instances entropy:

[begin{equation}
F = [E] -T H
tag{16}
finish{equation}]

Then, the additional free power of a system (Q) – in comparison with a system in equilibrium (P) – is proportional to their KL divergence, that’s, the knowledge of (Q) relative to (P):

[begin{equation}
F(Q) – F(P) = k T KL(q||p)
tag{17}
end{equation}]

Talking of free power, there’s additionally the – not uncontroversial – free power precept posited in neuroscience.. However in some unspecified time in the future, we have now to cease, and we do it right here.

Conclusion

Wrapping up, this submit has tried to do three issues: Having in thoughts a reader with background primarily in deep studying, begin with the “routine” use in coaching variational autoencoders; then present the – in all probability much less acquainted – “different aspect”; and at last, present a synopsis of associated phrases and their functions.

In case you’re excited by digging deeper into the numerous varied functions, in a variety of various fields, no higher place to begin than from the Twitter thread, talked about above, that gave rise to this submit. Thanks for studying!