I am super familiar with the generative modeling space, have designed them and deployed to real prod, and I will say you’re right that generative modeling results can be hit or miss. But, I was more referring to the page layout, its unreliability (seemed like the author was live debugging in this thread?), poor UX (seems like people can’t easily figure out the page), etc. rather than anything about the model itself (though tbh I also don’t find Pokémon GAN Number 10,000 compelling as a topic). Fundamentally though you do have to always consider results from the latest papers with some baseline skepticism.
Could you give examples of using GAN in prod? Recently there was a discussion on Twitter about the reduced number of GAN papers in the last few years, and some people mentioned that they did not find adoption in industry.
Sure, my team and I created “style transfer” GANs to help people better understand particular data in light of other data (the latter of which is usually easier to understand but not always available in practice). It ended up getting strongly positive feedback from large stakeholders, we secured a large contract to deploy, and deployed / maintain it as a SaaS. I even got a patent for the work! I’m sorry I can’t be too much more specific. But, it’s also partially why I may come across as slightly annoyed with the presentation here — HuggingFace is 100% something with functionality I would have preferred to leverage versus my team needing to handle modeling, training, building, releasing, deploying, SLAs, etc. And I would love to support them. But all the presentations being rudimentary with poor UX makes it difficult for me to use them to convince people of this fact, so it’s harder for me to get buy in from finance than it might otherwise be if HF released polished, well thought out demos.
As an aside. I do not personally feel the future of generative modeling is in generative art or creating new Pokémon or things like that, categories which broadly seem like neat tricks without real world usefulness or at least adoption.
That's awesome. If I may ask, the data you operate on are still image-like grids or do you operate on more basic data types (e.g. strings)?
Personally I'm also working on an industrial application, using a CycleGAN-based system to augment real world data (e.g. training a network to "paint" an object so we can apply traditional computer vision techniques such as a HSV filter to locate the object). It's quite promising for this kind of application, albeit hard to fine-tune.
This is image based, not text based. It’s very useful for a number of applications!
I think your usecase is extremely promising assuming it results in better quality output than just running a modern object detector. Another usecase I don’t have bandwidth for, but would likely be very marketable, is similar to what you’re saying but to allow the use of traditional algos like sift or surf across modalities.
