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Amongst deep studying practitioners, *Kullback-Leibler divergence* (KL divergence) is maybe greatest identified for its position in coaching variational autoencoders (VAEs). To study an informative latent house, we don’t simply optimize for good reconstruction. Slightly, we additionally impose a previous on the latent distribution, and purpose to maintain them shut – usually, 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 go away it at that, nevertheless, 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 put up, we’ll check out that different aspect.

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

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

The far more modest objectives of this put up, then, are

- to shortly recap the position of KL divergence in coaching VAEs, and point out similar-in-character purposes;
- as an instance that extra playful, adventurous “different aspect” of its character; and
- in a not-so-entertaining, however – hopefully – helpful method, differentiate KL divergence from associated ideas similar 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 possibilities in response to 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)) will not be the identical as (D_{KL}(q||p)). (Which is why it’s a *divergence*, not a *distance*.) This side will play an necessary position in part 2 devoted to the “different aspect.”

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

## KL divergence, “villain”

In lots of machine studying algorithms, KL divergence seems within the context of *variational inference*. Usually, for real looking knowledge, actual computation of the posterior distribution is infeasible. Thus, some type of approximation is required. In variational inference, the true posterior (p^*) is approximated by an easier distribution, (q), from some tractable household. To make sure we’ve a great approximation, we reduce – in idea, at the least – the KL divergence of (q) relative to (p^*), thus changing inference by optimization.

In follow, 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 knowledge:

[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 reveals the optimization goal to be an higher sure on the adverse log-likelihood (NLL):

[begin{equation}

J(q) = D_{KL}(q||p^*) – log p(D)

tag{5}

end{equation}]

One more formulation – once more, see (Murphy 2012) for particulars – is the one we really 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 sure*. Within the VAE put up 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, usually a multivariate regular).

### Past VAEs

Generalizing this “conservative” motion sample of KL divergence past VAEs, we will say that it expresses the standard of approximations. An necessary space the place approximation takes place is (lossy) *compression*. KL divergence offers a technique to quantify how a lot info is misplaced once we compress knowledge.

Summing up, in these and related purposes, KL divergence is “dangerous” – though we don’t need it to be zero (or else, why trouble utilizing the algorithm?), we definitely wish to hold it low. So now, let’s see the opposite aspect.

## KL divergence, good man

In a second class of purposes, KL divergence will not be one thing to be minimized. In these domains, KL divergence is indicative of shock, disagreement, exploratory conduct, or studying: This actually is the attitude of *info acquire*.

### Shock

One area the place *shock*, not info per se, governs conduct 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 believe – not probably 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 info – shock is an integral a part of the idea.

Thus, with KL divergence linked to shock, and shock rooted within the basic strategy of Bayesian updating, a course of that might be used to explain the course of life itself, KL divergence itself turns into basic. We might get tempted to see it all over the place. 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 increased anticipated positive factors 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 doable as a result of like entropy, KL divergence is additive.

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

### Asymmetry in motion

Trying once more on the KL system

[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 completely different. For one, the expectation is computed over the primary distribution ((p) in (1)). This side is necessary as a result of the “order” (the respective roles) of (p) and (q) might must be chosen in response to tractability (which distribution can we common over).

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

To keep away from infinite shock, we will be sure that our prior likelihood isn’t zero. However even then, the fascinating factor is that how a lot info we acquire in anybody occasion is dependent upon *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 most likely don’t exist, however they may … possibly 1 p.c of them is black. Put otherwise, 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 data 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 chances had been 50:50. On seeing a black swan, I get quite a bit 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 may lead one to seeing it in motion all over the place. Nevertheless, we nonetheless have the third and remaining process to deal with: shortly 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 typical 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 all which is what we generally image as its “essence,” and one in all which is particularly fascinating.

As to the previous, if there are (n) doable 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’ve 16 doable 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 principally 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 possibilities.

Subjectively, entropy displays our uncertainty whether or not an occasion will occur. Apparently although, it exists within the bodily world as effectively: 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 circumstances, not be one of the best technique of characterizing human conduct. Going again to the eyetracking instance, it’s fully intuitive that individuals take a look at stunning components of photos, not at white noise areas, that are the utmost you can get when it comes to entropy.

As a deep studying practitioner, you’ve most likely been ready for the purpose at which we’d point out *cross entropy* – probably 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 applied your individual classification community, you most likely 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 necessary amount, utilized in many contexts and purposes, is *mutual info*. Once more citing DeDeo, “you possibly can consider it as probably the most basic type of correlation coefficient that you would be able to measure.”

With two variables (X) and (Y), we will ask: How a lot can we study (X) once we study 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) provides you about (Y) is the same as that (Y) provides 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 shortly look one other information-theoretic measure that not like 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 have to be symmetric, and it should obey the triangle inequality.

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

[begin{equation}

m_i = frac{1}{2}(p_i + q_i)

tag{14}

end{equation}]

the Jensen-Shannon distance is a mean of KL divergences, one in all (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 thinking about (undirected) distance between, not directed shock brought on by, distributions.

Lastly, let’s wrap up with a final time period, proscribing ourselves to a fast glimpse at one thing entire books might 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 already know what it stands for, is simply what it’s), but additionally, 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 adverse the ELBO, that’s, corresponds to equation (2). However for these , there may be *free power* as a central idea in thermodynamics.

On this put up, we’re primarily thinking about how ideas are associated to KL divergence, and for this, we observe the characterization John Baez provides in his aforementioned discuss.

*Free* power, that’s, power in helpful type, is the anticipated power minus temperature occasions 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 sooner or later, we’ve to cease, and we do it right here.

## Conclusion

Wrapping up, this put up 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 – most likely much less acquainted – “different aspect”; and at last, present a synopsis of associated phrases and their purposes.

In case you’re thinking about digging deeper into the various varied purposes, in a spread of various fields, no higher place to start out than from the Twitter thread, talked about above, that gave rise to this put up. Thanks for studying!

DeDeo, Simon. 2016. “Data Concept for Clever Individuals.”

*Nature Opinions. Neuroscience*11 (February): 127–38. https://doi.org/10.1038/nrn2787.

*Advances in Neural Data Processing Techniques 18 [Neural Information Processing Systems, NIPS 2005, December 5-8, 2005, Vancouver, British Columbia, Canada]*, 547–54. http://papers.nips.cc/paper/2822-bayesian-surprise-attracts-human-attention.

Murphy, Kevin. 2012. *Machine Studying: A Probabilistic Perspective*. MIT Press.

*CoRR*abs/1103.5708. http://arxiv.org/abs/1103.5708.

Zanardo, Enrico. 2017. “HOW TO MEASURE DISAGREEMENT ?” In.

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