## Generative Models

Consider a very simple generative model consisting of a single variable B representing the probability of a biased coin toss landing heads up. We're given a large data set of recorded coin flips and are tasked with fitting the model to the data, i.e. finding the value of B that best represents the data (or more specifically, that best represents the system from which the data was sampled).

The data set exhibits a 75% / 25% distribution between heads and tails respectively, we therefore conclude that the optimal value for our model variable B is 0.75:

$ \hat{\Bbb E}[B] = \frac{N_{heads}}{N} \tag{1}$

I.e. The proportion of flips that are heads gives the estimate 0.75 for the expected value of B.

NB.The exact expected value for B is given by:

$ B = \frac{N_{heads} + 1}{N + 2} \tag{2}$

See Estimating a Biased Coin for a derivation.

Suffice to say that for large N the estimate of B based on a simple ratio is a good one, but poor for very small values of N.

If we now run our model in generative mode (i.e. take random samples from it) we observe that approximately 75% of the tosses are heads as per the real coin, i.e. our model generates data with a distribution very similar to that of the system it is modeling. So far, so good.

**Big Models**

Unfortunately our coin flip model with a single variable isn't very useful. If we wish to model large datasets of images, audio, text, etc. and the complex hierarchy of structures inherent to those types of data (e.g. understanding that some subset of pixels in an image represent a kitten, another subset represent a ball of string, and that kittens and balls of string are often observed together) then we need a lot more model variables. Contemporary models may contain hundreds of millions or even billions of variables, and there is no simple mathematical expression (such as our equation 2) that will give us the optimal value for each of them. In such cases the go-to method is that of gradient following (most commonly referred to as Gradient Descent).

**Gradient Descent**

The basic idea is that we have some means of measuring the quality of a given model with respect to some goal, i.e. what we want the model to do. Using such a quality metric we can determine, for any given single model parameter, how the model quality changes with small changes to that parameter. We can then make lots of tiny incremental updates to the each of the millions of parameters and slowly 'nudge' the model towards the desired goal. It turns out this approach can work very well, although caveats apply.

In short, we calculate the gradient (i.e. the first order derivative) of the quality metric with respect to a model variable, and follow the gradient in tiny steps. That is, rather than the equation 1 approach of jumping directly to a directly calculated optimal value, instead we move incrementally in a direction towards where we think the optimal value will be, noting that each variable is being updated simultaneously with millions of others and therefore our gradient estimate for each variable is changing as all of those other variables change.

Notes

Quality here is a single scalar value that we can calculate for a whole model, it is a value we wish to maximise. Actually the term 'quality' isn't in wide use, but I think it best captures what this value is. Terms you will more likely see used are Error, Energy or Entropy; noting that we want to minimise error (hence the term gradientdescent).

A single quality value considered alone generally doesn't have significant meaning. A quality metric gives us a way of determining if some model is better or worse than some other model, and therefore is able to guide the gradient descent learning towards better models. After some period of time, or once gradient descent is no longer finding better models, we can begin testing the learned model to see how well it performs; the quality metric no longer has any purpose, it was solely a means of guiding the learning process.

**Supervised Models**

In Supervised Learning a model will typically produce an output vector for a given input vector, and the training data is in the form of input-output pairs. In that world there's a natural choice of quality, i.e. how close each produced output vector is to the correct (target) output vector. The error at the outputs is a simple pointwise subtraction of the target vector from the produced vector, it's then necessary to communicate that error back through the model in order to calculate the error gradient at each variable. As we go further back into layers of the model the error signal can become greatly diminished and noisy, and this has been a long running issue and focus of research (see backpropagation).

The need for target vectors also causes problems, e.g. for the inputs we might have a set of raw images, and for the corresponding outputs we might have a series of labels of things (i.e. binary categories) for each image (a ball of string, a kitten...). The choice of labels will typically be somewhat artificial in that they're defined by people and what they think the relevant features are in each image, therefore the choice of labels may not be fully representative of everything 'going on' in an image that is relevant to the model we wish to build, after all a picture is worth a thousand words.

E.g. if we want to create a general purpose image recognition model then we want to model *everything* in an image - the fluffiness of the cat's fur,
the texture of the carpet, the subtle change in light tone across the room, the wound fibres that make up the string, the relative positions
of the cat and the ball of string, the angle a ray of sunlight makes across a wall, the shadow cast by a vase on the windowsill ... and so on.

Another problem with image labels in particular is that ideally we would be localising them, so *this* subset of pixels represent the cat, rather than
*there's a cat somewhere in this image, let's get the model figure out where*. Images are typically (currently) modeled by convolutional nets, i.e.
an image is broken down into sub-regions (usually overlapping square regions), and ideally we would be assigning our 'cat' label to the correct regions.
So the problem of producing good labels is not just the choice of labels, but also which regions of an image they apply to. In non-image data sets the same
problem generally applies (label localisation), i.e. this problem is not specific to image data.

**Generative Models**

In contrast to supervised models, generative models have no need for input-output pairs, instead they operate on sets of single training vectors. Each training vector is considered a sample from the system being modeled, and our task is to find model parameters that result in a model that generates vectors with the same distribution as the training data, as per our coin flip example above.

At first it may seem that such a model has no purpose since it appears that we cannot query it. We could present a supervised model with a raw image (input vector) and it would produce a set of labels (kitten, ball of string...); in contrast the generative model just generates pictures of cats and other things it has observed in the data, what's the point of that?

**Pattern Completion**

A simple way of addressing that apparent deficiency is to include labels in the training vectors, so we just combine the input-output vector pairs from the supervised world into a single vector, i.e. we're saying that each image is coincident with some set of labels - model that. Now our model will generate vectors that consist of a random image alongside labels that go with it. Interesting, but still not immediately useful. However we can now query this model by presenting it with partial vectors (e.g. an image but no labels) and asking it to 'complete' the vector; whether this is possible depends on the type of model, but broadly speaking we can devise types of model where a partial vector represents a prior on the set of possible generations. As such we can ask a model to complete partial vectors, i.e. given this image, what labels are probable? However, that still leaves the problem of people choosing what labels to use, and almost certainly producing a set of labels that are deficient in quantity and quality.

Note however that pattern completion also works the other way, e.g. here are some labels, generate some images that would generally co-occur
with them. Or, here is a partial image, attempt to complete it *and* produce some relevant labels that describe what the model thought it saw.
We usually can't use supervised models in this way because (A) they're fundamentally devised to handle one way mappings, and (B) the mappings
are based on a deficient set of labels.

**Automatic Feature Discovery**

Labels are just categories, some binary variable that can be on or off, you either have it or you don't. Some labels a person might devise are -
*cat, dog, car, house, chair, table, leg, red, blue, etc.* Note that these are somewhat high level, we haven't defined 'cat paw',
'left front cat paw', 'cat whisker', 'floppy left cat ear', 'car wheel nut', etc. We could do but it's highly impractical for a person to assign these labels (and
their location in the image). In fact there's a hierarchy of categories, e.g. room -> floor -> cat -> 'cat leg' -> paw -> claw. Why not
define were each of these things are and how they relate to the other things? Given that that's how the world is structured and that we are trying to model
images from the world, it seems reasonable that our model would use such a scheme to represent the world.

To some extent supervised models are learning to represent the training data by learning that certain image features we didn't tell it about are
co-incident, e.g. a model could learn to identify cat legs and that they're usually attached to cats, even though we didn't manually assign a label
for 'cat leg'. In the generative model world we require the model to learn *all* features automatically via gradient descent learning. And because these
features are automatically learned they can be more numerous, they can be properly localised (this set of pixels relates to a cat leg), and they can form
probabilistic associations (a cat leg is nearly always co-incident with whole cats, and often co-incident with balls of string).
This is probably the major key distinction between supervised and generative models with regards to their efficacy, and scalability. Generative models
take raw data and discover the features in that data that best represent it; supervised models... don't, and require significant manual effort to build
the training data sets.

**Gradient of Generative Models**

The elephant in the room is that I've yet to mention what the quality metric is for generative models. For supervised models we have the error between the target and produced vectors and this forms the basis of a quality metric; how do we arrive at an error value for a generative model?

The short answer is that we use the difference between the generated probability distribution and the target distribution (i.e the distribution observed in the training data). It's perhaps helpful to start with the coin flip domain, i.e. e.g. if B=0 then we have zero probability of creating the data set H-H-T-H, and in fact we can, for a given value of B, calculate the probability that our model will generate coin tosses with the same distribution as the training data, and also the gradient of that function with respect to a model variable; and so we have a gradient function to plug-in to a gradient descent learning algorithm.

The ideas in, and inspiration for this post derive heavily from:

*
Colin,
August 27, 2016
*

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