Simulated data: the great equalizer in the AI race?

You are ignoring the fact that generative AI is not closed-loop algorithm. You can synthesize expected features in a data set and feed them to the detector - out of bounds of the generative neural network that rather serves the purpose of mapping into (a subset of) the proper input space.

The power of synthesis is not within the GAN or VAE, it is in the outside mechanism that guides the creation of content with specific domain knowledge about the feature space.

This might not replace the value of real data, but it will allow to accelerate bootstrap, improve coverage (at cost of accuracy), or provide free environments for auxiliary processes like CI/CD in many deep learning applications.

There is a lot of published material on synthetic data augmentation if you actually look for it.

It's not about creating information in the information-theoretic sense. It's that nobody knows how neural networks work. So even if, say, all of the knowledge to recognize that this is a screwdriver is present in the 3D model, it's not like we know how to train a neural network to detect those features from a 2D camera image. So we just generate a gazillion 2D images in various rotations and lighting conditions and let the neural network use its black magic. With no "backchannel" into the "brains" of the neural network, we can't tell it to recognize black people's emotions the same way it recognizes white people's emotions. So if we don't have enough black people in our training set, we build a way to simulate an image of a black person and hammer it into the neural network's brainstem.

> Information theoretically speaking, how do you generate a "synthetic" dataset (as the article calls it) with the same fidelity as an original dataset without having access to a critical basis set of the original?

Information theoretically speaking, this is impossible. The synthetic dataset will always have exploitable mathematical properties in a way non-synthetic dataset will not. It will open up the trained model to easy adversarial attacks.

> Information theoretically speaking, how do you generate a "synthetic" dataset (as the article calls it) with the same fidelity as an original dataset without having access to a critical basis set of the original?

You don't. It's not useless as a technique, but it is limited. IMO more limited than the article's author presents, but they do touch on some of the useful bits.

Cool points -

"...original dataset without having access to a critical basis set of the original?"

I think that they're not trying to copy existing datasets but are trying to generate new datasets that solve various computer vision use-cases. Looks lke they're using 3D photorealistic models and environments to then generate 2D data. It is a cool idea, if they had the ability to synthesize a large amount of 3D people and objects and insert them into 3D environment in ways that made sense and then run motion simulation, they could hypothetically create an incredible amount of high-quality data. Sounds pretty hard to do honestly...

I think Monte Carlo is used for something very different than computer vision / machine learning. Monte Carlo is usually used to estimate an average result given many dependent variables and a simplified model of the problem. So if I want to estimate how far my paper airplane will fly and I have a simulator, I would vary the paper thickness, folds and wind. Each time I would run the simulator, get a result and then I can estimate the average distance the paper airplane would go! (actually sounds like a fun project lol). Anyway this is just different.

Simulation is good for edge cases because you can simulate them disproportionally to their prevalence in the real world. So let's say that we're in a smart store and we want to recognize when an elderly person falls on the floor to send human help to the correct location. This happens maybe one in 5 year in a given store. If we were to gather data we may get 10 examples. If they can simulate this, they could simulate 100k elderly people falling and then train models to recognize it! Kind of crazy really.

All good points. In this case, the original dataset is created from real world body scans. You collect enough scans in this "base collection of scans" to have a "real" distribution of the world. You can then span a latent space on top of this initial distribution and use GANs to further scale it. This isn't as good as real yet, but it generates results that are better than limited quantities of real data alone. Agree with your point around the Monte Carlo simulation. Synthetic data is not the be-all end-all to train neural networks.

How you do it is similar to this talk: https://youtu.be/MiiWzJE0fEA

It's not perfect. You do need real world data. Synthetic data is great for creating edge cases. Say you want your model to classify more general use cases, but your real world data is limited. You can generate data that fits edge cases with some variance and the model will accept more variety, reducing over fitting.

Interpolation between known sets? You could have an envelope of real world conditions and create thousands of samples that are variations in between for purposes of training.

Sounds like a pretty bad experience indeed - surprised that was their recommendation vs completely anonymizing the data. You didn't share if you went ahead with it and saw any results? If so, it sounds like they were not good. Either way, I don't think you can let one bad experience cast doubt on a whole field. There are plenty of example of medical research institutes using synthetic data in combination with real patient data to improve their neural nets. I'm no medical expert, but data augmentation or full simulation works when it's used in the right context. Having said that creating biased algorithms that generate biased data is certainly a reality as well.

You could simulate your own data that disagrees with their simulated data and thus draw attention to the absurdity of the whole situation.

def not for every situation. I was happy to see that these guys weren't starting from medical use-cases. Way too hard.

Good points!

Most data is def crap lol.

I don't see few shot, one-shot or no-shot getting anywhere close to standard supervised learning for anything practical. It really doesn't make sense in production settings at all.

You have a function that you want to learn, let's say mapping between an RGB image to a segmentation map. For most applications you're never really in a situation where a production product is dealing with visual scenes/objects it has never seen before. In a factory, in smart stores, in cars, AR scenarios like I just don't see it happening. And then if this case is removed, I'm thinking, ok so when can I get good enough results from a tiny dataset? Machine Learning isn't magic, you're trying to learn a function with 100 million parameters using a dataset, I just don't see the math working out. More data provides better results, it inserts more information to create a more relevant mapping function from input to output.

Third point is great! As long as the models are somehow based on real-world scans I think a lot of good can come from this. The funny thing is, there is so much bias in networks trained today precisely because the data captured is usually small and captured from a specific area/population/setting. If you had a great synthetic data generation engine you could at least generate equal representation of gender, age groups, ethnicities, ... etc.

Overall great points!

All valid points - thanks for the comment.

1. Totally agree with the point about data science talent being one of the bottlenecks. And in that area, the big tech companies already have the best & brightest workforce. Most data is crap is a bit of a stretch, but agree that a lot of pre-processing is required. But more and more platforms and/or services company are filling this gap.

2. 100%. This is a short term problem.

3. Agree to some extent. You can span latent spaces on top of an initial batch to overcome scarcity and use GANs to scale up. Data augmentation is not perfect but solves a lot of problems in production. Also specifically as it relates to computer vision, it's "easier" to know what the real world looks like and try to replicate it. It's hard in practice but the assumptions are mostly agreed upon.

Exactly this. Amazing, a picture of a white-mans arm holding a box of orange juice. But what if the person is a woman, or dark skinned, or has prosthetics, or is a child, or it's a bag/bottle instead of a box, or the lighting is different, or the camera is low resolution, or there is someone standing in the way...

Anyone in this space is well aware that the benefit of big data isn't just the amount of data, but that's it's a real representative sample of the type of data you are actually going to need to work with. Big data solves the problem of people being bad at creating simulations. To suggest simulations as a solution to big data is kinda getting the relationship backwards.

Yeah but you're assuming that you know how their creating this data. Just throwing an option out there - what if they created a latent space of 3D people and iteratively expanded it with GANs and 2D real image datasets. That would generalize.

Just a thought, not sure what's really going on there, I just know that they probably have something interesting they're cooking up!

This is a really crazy vision

Disagree, otherwise I obviously wouldn't have written this piece. ;) Synthetic data (SD) is not a silver bullet that will solve all problems, but it opens up a lot of opportunities. I'm seeing cool startups using SD to accelerate their R&D efforts and launch products in production in ways I didn't see 2 years ago. Imho, the quality of SD is reaching a tipping point and the sim2real gap is starting to disappear.

I read about the guy who used GTA to train a neural net. I think he was trying to make the point that although obviously imperfect, using simulated data could actually work. I'm not saying using simulated data (SD) should the be-all end-all for training neural nets, but we're seeing algorithms performing better when they're trained using a combination of real labelled data and SD rather than real data alone. I hear your point though about hype cycles and the tunnel vision SV can often fall into.

Highly interesting thought! Based on Psychology's understanding of dreams I would guess that sleep is actually rather a replay of real experience to stabilize our neural network, but most probably augmented with synthetic variations of the experience data.

references?