7 ms·

Synthetic Data from Diffusion Models Improves ImageNet Classification

78 points by tim_sw 4y ago

cs702 4y ago
My immediate thoughts: 1. The human brain must be doing something analogous to this, continuously, to learn from a relatively small numbers of samples. 2. Going forward, generating high-quality synthetic data looks likely to become a standard practice for training models. 3. Whoever has the largest, highest-quality synthetically generated datasets will have the best-performing AI models.
- version_five 4y ago
  Re (3) I almost think it implies the opposite: if everyone has access to synthetic data from large foundation models, it will be those that have their own proprietary data that can stand out. This is already true imo. There are lots of competing models with similar performance on imagenet, but when you get into niche computer vision, data is the main differentiator.
  - cs702 4y ago
    If, as you write, "everyone has access to synthetic data from large foundation models," then I'd agree. But I expect we're going to see a transition in the opposite direction, with more corporations keeping newer models and data proprietary to maximize competitive advantage. I suspect this kind of thinking factored into OpenAI's decision not to release any details about GPT-4.
    - version_five 4y ago
      Fair enough. I think that many would like to have their own proprietary models, but for now, these "web scale" datasets really only come from scraping the internet. Companies can do their own cleaning and whatnot, but the data distribution still largely reflects what's on the internet because that's all there is. To your point though, three are definitely some proprietary pockets, like the Shutterstock/openai thing. Or the various RL layers ala chatgpt. But a lot of the value there is in the labeling, and I expect we'll see more competing versions that are open (we already are)
      - cs702 4y ago
        I hope you're right :-)
- fnordpiglet 4y ago
  You mean like dreaming?
- cubefox 4y ago
  Training on synthetic data has the problem that the models may drift away from the original distribution because errors could get amplified.
version_five 4y ago
First reactions before reading in detail: Does it give more lift than just using the training data from the diffusion model? You can search through LAION 5B in Clip embedding space and pull out lots of new training data without ever using a generative model. Are they able to use prompts to generate augmentations around whatever invariant features - different colors or positions or scenes- in order to train better?
- minihat 4y ago
  This is the right question. It is plausible to me that this is a way of condensing a vast training set into a reduced set of high quality examples.
  - version_five 4y ago
    You could also consider some kinds of sampling of the dataset - basically not including images that are too close together in whatever space. There are also data distillation methods that are supposed to generate a smaller training set - the ones I've seen don't generate images that look like real training samples though. I think it's definitely an interesting thing to study - it would be worth exploring what specifically the generative model can add. I've actually done something like this with GANs, but the gains came largely from generating plausible training images that were augmentations around some invariance, as I mentioned above.
- sharemywin 4y ago
  is there an un-similarity search using clip. some reasonably efficient way to pull the X un-similar embeddings(to Y dataset) in Z dataset?
  - version_five 4y ago
    Un-similarity would generally just mean further away in the latent space. I don't know if there are common heuristics used for this - taking the furthest away would give you outliers, and then probably lots of members of whatever class was furthest away. So you'd probably be best first clustering in the latent space and then sampling from each cluster.
ttul 4y ago
Sam Altman and Ilya Sutskever have made several mentions of the idea that models are better at teaching models than humans are, in many cases. The next GPT iteration will involve a lot of AI-teaching-AI.
- unshavedyak 4y ago
  Is there a high level explanation as to why? Best I can imagine it’s that it’s easier to improve a model than make one from scratch, and so AI training AI means you can start fresh and have the benefits of the previous version train the foundation for the next, and then yet again refine that new version and repeat. … but this is 100% speculation, I have no clue. Thoughts?
  - thomashop 4y ago
    Probably because you can use the error gradients from the generative model somehow
  - visarga 4y ago
    The task of generating images is harder than that of classifying, the training signal is also much denser. So it means the diffusion model implicitly learned a better visual model, and now they found a way to invert that and improve the discriminative task as well. We can do the same thing in text. A recent paper showed GPT-4 is better than most human labellers at tagging NLP datasets. I personally used it to generate samples for smaller models, basically the same thing this paper showed. You can even top that with a second LLM round to reflect on its generated data and filter the noisy examples. In the end it still makes errors. And the errors GPT makes are hard to detect at scale.
    - bagels 4y ago
      Can you give an outline of how you used gpt4 to generate samples?
- ShamelessC 4y ago
  Alan Turing actually talked about this way back in Computing Machinery and Intelligence and it’s already used for distillation of information from large models to more compact ones in the so called “student/teacher” training regime.
HopenHeyHi 4y ago
[flagged]
nextworddev 4y ago
This is one of the reasons why we are probably not going to "run out of tokens" to push transformers to the next level. And we haven't even started really using visual data as inputs to train auto-regressive multi-modal LLMs.
- williamtrask 4y ago
  There's no free lunch when it comes to learning signal. The right comparison here is between training on synthetic data and training on the data used to create the synthetic data generator. Synthetic data doesn't create more learning signal, it just creates more data repeating the same learning signal with some bias assumptions that might as well be in your final model. The difference is moreso in training times for retraining new models. If you've taken highly disparate data and compressed it into a particularly "juicy" dataset which is smaller, that could bring down training times. That is to say, it's related to curriculum learning moreso than breaking information theory.
  - bheadmaster 4y ago
    If a model truly generalizes, why wouldn't it be possible for it to generate more real signal than was used for training it? Isn't that the whole point of using models instead of, I don't know, huge lookup tables?
    - tempusalaria 4y ago
      The theory behind models is that they learn the distribution used to “generate” the data. A good model has a good approximation of the distribution. But then anything generated from it follows the approximation of the distribution not the distribution itself I.e. the generated data is not going to be good training data for the generating model. You can for example train a very large and good model and use that to generate more data to train a smaller model e.g. for a faster inference use case. That data follows a closer approximation of the underlying distribution than the small model has so it can still be used for convergence to the underlying distribution.
bbstats 4y ago
seems logical in that you can train on more poses/colors/shapes/angles/lighting conditions. but also we could assume that there's a more direct/elegant improvement than using a generative model?
backflippinbozo 4y ago
Exciting news, thanks for sharing! We've been applying this technique to create custom models on the fly with our no code platform at https://remyx.ai https://remyx.ai We're trying to build up our user base and get more feedback, try it out or check out our walkthrough: https://youtu.be/7SMySnRRTew?t=39 https://youtu.be/7SMySnRRTew?t=39
dylan808hewitt 4y ago
[dead]
yreg 4y ago
I believe Tesla uses synthetic data for FSD/Autopilot training as well. Not sure what's the ratio between synthetic data and real videos, but it seems that they plan to rely more onto the first group in the future. https://www.youtube.com/live/j0z4FweCy4M?feature=share&t=5717 https://www.youtube.com/live/j0z4FweCy4M?feature=share&t=571...

78 points by tim_sw 4y ago

My immediate thoughts: 1. The human brain must be doing something analogous to this, continuously, to learn from a relatively small numbers of samples. 2. Going forward, generating high-quality synthetic data looks likely to become a standard practice for training models. 3. Whoever has the largest, highest-quality synthetically generated datasets will have the best-performing AI models.

version_five 4y ago
Re (3) I almost think it implies the opposite: if everyone has access to synthetic data from large foundation models, it will be those that have their own proprietary data that can stand out. This is already true imo. There are lots of competing models with similar performance on imagenet, but when you get into niche computer vision, data is the main differentiator.
- cs702 4y ago
  If, as you write, "everyone has access to synthetic data from large foundation models," then I'd agree. But I expect we're going to see a transition in the opposite direction, with more corporations keeping newer models and data proprietary to maximize competitive advantage. I suspect this kind of thinking factored into OpenAI's decision not to release any details about GPT-4.
  - version_five 4y ago
    Fair enough. I think that many would like to have their own proprietary models, but for now, these "web scale" datasets really only come from scraping the internet. Companies can do their own cleaning and whatnot, but the data distribution still largely reflects what's on the internet because that's all there is. To your point though, three are definitely some proprietary pockets, like the Shutterstock/openai thing. Or the various RL layers ala chatgpt. But a lot of the value there is in the labeling, and I expect we'll see more competing versions that are open (we already are)
    - cs702 4y ago
      I hope you're right :-)
fnordpiglet 4y ago
You mean like dreaming?
cubefox 4y ago
Training on synthetic data has the problem that the models may drift away from the original distribution because errors could get amplified.

version_five 4y ago

First reactions before reading in detail: Does it give more lift than just using the training data from the diffusion model? You can search through LAION 5B in Clip embedding space and pull out lots of new training data without ever using a generative model. Are they able to use prompts to generate augmentations around whatever invariant features - different colors or positions or scenes- in order to train better?

minihat 4y ago
This is the right question. It is plausible to me that this is a way of condensing a vast training set into a reduced set of high quality examples.
- version_five 4y ago
  You could also consider some kinds of sampling of the dataset - basically not including images that are too close together in whatever space. There are also data distillation methods that are supposed to generate a smaller training set - the ones I've seen don't generate images that look like real training samples though. I think it's definitely an interesting thing to study - it would be worth exploring what specifically the generative model can add. I've actually done something like this with GANs, but the gains came largely from generating plausible training images that were augmentations around some invariance, as I mentioned above.
sharemywin 4y ago
is there an un-similarity search using clip. some reasonably efficient way to pull the X un-similar embeddings(to Y dataset) in Z dataset?
- version_five 4y ago
  Un-similarity would generally just mean further away in the latent space. I don't know if there are common heuristics used for this - taking the furthest away would give you outliers, and then probably lots of members of whatever class was furthest away. So you'd probably be best first clustering in the latent space and then sampling from each cluster.

ttul 4y ago

Sam Altman and Ilya Sutskever have made several mentions of the idea that models are better at teaching models than humans are, in many cases. The next GPT iteration will involve a lot of AI-teaching-AI.

unshavedyak 4y ago
Is there a high level explanation as to why? Best I can imagine it’s that it’s easier to improve a model than make one from scratch, and so AI training AI means you can start fresh and have the benefits of the previous version train the foundation for the next, and then yet again refine that new version and repeat. … but this is 100% speculation, I have no clue. Thoughts?
- thomashop 4y ago
  Probably because you can use the error gradients from the generative model somehow
- visarga 4y ago
  The task of generating images is harder than that of classifying, the training signal is also much denser. So it means the diffusion model implicitly learned a better visual model, and now they found a way to invert that and improve the discriminative task as well. We can do the same thing in text. A recent paper showed GPT-4 is better than most human labellers at tagging NLP datasets. I personally used it to generate samples for smaller models, basically the same thing this paper showed. You can even top that with a second LLM round to reflect on its generated data and filter the noisy examples. In the end it still makes errors. And the errors GPT makes are hard to detect at scale.
  - bagels 4y ago
    Can you give an outline of how you used gpt4 to generate samples?
ShamelessC 4y ago
Alan Turing actually talked about this way back in Computing Machinery and Intelligence and it’s already used for distillation of information from large models to more compact ones in the so called “student/teacher” training regime.

HopenHeyHi 4y ago

[flagged]

nextworddev 4y ago

This is one of the reasons why we are probably not going to "run out of tokens" to push transformers to the next level. And we haven't even started really using visual data as inputs to train auto-regressive multi-modal LLMs.

williamtrask 4y ago
There's no free lunch when it comes to learning signal. The right comparison here is between training on synthetic data and training on the data used to create the synthetic data generator. Synthetic data doesn't create more learning signal, it just creates more data repeating the same learning signal with some bias assumptions that might as well be in your final model. The difference is moreso in training times for retraining new models. If you've taken highly disparate data and compressed it into a particularly "juicy" dataset which is smaller, that could bring down training times. That is to say, it's related to curriculum learning moreso than breaking information theory.
- bheadmaster 4y ago
  If a model truly generalizes, why wouldn't it be possible for it to generate more real signal than was used for training it? Isn't that the whole point of using models instead of, I don't know, huge lookup tables?
  - tempusalaria 4y ago
    The theory behind models is that they learn the distribution used to “generate” the data. A good model has a good approximation of the distribution. But then anything generated from it follows the approximation of the distribution not the distribution itself I.e. the generated data is not going to be good training data for the generating model. You can for example train a very large and good model and use that to generate more data to train a smaller model e.g. for a faster inference use case. That data follows a closer approximation of the underlying distribution than the small model has so it can still be used for convergence to the underlying distribution.

bbstats 4y ago

seems logical in that you can train on more poses/colors/shapes/angles/lighting conditions. but also we could assume that there's a more direct/elegant improvement than using a generative model?

backflippinbozo 4y ago

Exciting news, thanks for sharing! We've been applying this technique to create custom models on the fly with our no code platform at https://remyx.ai https://remyx.ai We're trying to build up our user base and get more feedback, try it out or check out our walkthrough: https://youtu.be/7SMySnRRTew?t=39 https://youtu.be/7SMySnRRTew?t=39

dylan808hewitt 4y ago

[dead]

yreg 4y ago

I believe Tesla uses synthetic data for FSD/Autopilot training as well. Not sure what's the ratio between synthetic data and real videos, but it seems that they plan to rely more onto the first group in the future. https://www.youtube.com/live/j0z4FweCy4M?feature=share&t=5717 https://www.youtube.com/live/j0z4FweCy4M?feature=share&t=571...