GANs Training: Generate Images Following a Sketch

Make GANs training easier for everyone by generating Images following a sketch!

Indeed, whit this new method, you can control your GAN's outputs based on the simplest type of knowledge you could provide it: hand-drawn sketches.

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Machine learning models can now generate new images based on what it has seen from an existing

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set of images.

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We can't really say that the model is creative, as even though the image is indeed new, the

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results are always highly inspired by similar photos it has seen in the past.

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Such a type of architecture is called a generative adversarial network, or GAN.

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If you already know how GANs work, you can skip to this time to see what the researchers

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did.

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If not, I'll quickly go over how it works.

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This powerful architecture basically takes a bunch of images and tries to imitate them.

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There are typically two networks, the generator, and the discriminator.

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Their names are pretty informative...

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The generator tries to generate a new image, and the discriminator tries to discriminate

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such images.

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The training process goes as follows: the discriminator is shown either an image coming

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from our training dataset, which is our set of real images, or an image made by the generator

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called a fake image.

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Then, the discriminator tries to say whether the image was real or fake.

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If the image sent guessed real was fake, we say that the discriminator has been fooled,

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and we update its parameters to improve its detection ability for the next try.

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In reverse, if the discriminator guessed right, saying it was fake, the generator is penalized

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and updated the same way, thus improving the quality of the future generated image.

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This process is repeated over and over until the discriminator is fooled half the time,

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meaning that the generated images are very similar to what we have in our real dataset.

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So the generated images now look like they were picked from our dataset, having the same

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style.

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If you'd like to have more details about how a generator and a discriminator model work

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and what they look like on the inside, I'd recommend watching one of the many videos

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I made covering them, like this one appearing on the top right corner

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right now...

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The problem here is that this process has been a black box for a while, and it is extremely

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difficult to train, especially to control what kind of images are generated.

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There has been a lot of progress in understanding what part of the generator network is responsible

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for what.

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Traditionally, building a model with control on the generated images' style to produce

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what we want, like generating images of cats with a specific position,

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needs specialized knowledge in deep learning, engineering work, patience, and a lot of trial

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and error.

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It would also need a lot of image examples, manually curated, of what you aim to generate

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and a great understanding of how the model works to adapt it for your own needs correctly.

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And repeat this process for any change you would like to make.

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Instead, this new method by Sheng-Yu Wang et al. from Carnegie Mellon University and

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MIT called Sketch Your Own GAN can take an existing model, for example, a generator trained

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to generate new images of cats, and control the output based on the simplest

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type of knowledge you could provide it: hand-drawn sketches.

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Something anyone can do, making GANs training a lot more accessible.

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No more hard work and model tweaking for hours to generate the cat in the position you wanted

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by figuring out which part of the model is in charge of which component in the image!

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How cool is that?

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It surely at least deserves a like on this video and sending it to your group chat!

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;) Of course, there's nothing special in generating

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a cat in a specific position, but imagine how powerful this can be.

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It can take a model trained to generate anything, and from a handful of sketches, control what

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will appear while conserving the other details and the same style!

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It is an architecture to re-train a generator model, encouraging it to produce images with

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the structure provided by the sketches while preserving the original model’s diversity

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and the maximum image quality possible.

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This is also called fine-tuning a model, where you take a powerful existing model and adapt

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it to perform better for your task.

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Imagine you really wanted to build a gabled church but didn't know the colors or specific

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architecture?

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Just send the sketch to the model and get infinite inspiration for your creation!

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Of course, this is still early research, and it will always follow the style in your dataset

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you used to train the generator, but still, the images are all *new* and can be surprisingly

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beautiful!

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But how did they do that?

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What have they figured out about generative models that can be taken advantage of to control

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the output?

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There are various challenges for such a task, like the amount of data and the model expertise

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needed.

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The data problem is fixed by using a model that was already trained, which we are simply

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trying to adapt to our task using a handful of sketches instead of hundreds or thousands

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of sketches and image pairs which are typically needed.

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To attack the expertise problem, instead of manually figuring out the changes to make

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to the model, they transform the generated image into a sketch representation using another

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model trained to do that, called Photosketch.

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Then, the generator is trained similarly to a traditional GAN training but with two discriminators

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instead of one.

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The first discriminator is used to control the quality of the output, just like a regular

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GAN architecture would have following the same training process we described earlier.

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The second discriminator is trained to tell the difference between the generated sketches

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and the sketches made by the user.

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Thus encouraging the generated images to match the user sketches structure similarly to how

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the first discriminator encourages the generated images to match the images in the initial

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training dataset.

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This way, the model figures out by itself which parameters to change to fit this new

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task of imitating the sketches and removing the model expertise requirements to play with

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generative models.

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This field of research is exciting, allowing anyone to play with generative models and

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control the outputs.

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It is much closer to something that could be useful in the real world than the initial

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models, where you would need a lot of time, money, and expertise to build a model able

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to generate such images.

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Instead, from a handful of sketches anyone can do, the resulting model can produce an

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infinite number of new images that resemble the input sketches allowing many more people

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to play with these generative networks.

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Let me know what you think and if this seems as exciting to you as it is to me!

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If you'd like more detail on this technique, I'd strongly recommend reading their paper

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linked in the description below!

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Thank you for watching.

►Read the full article: https://www.louisbouchard.ai/make-gans-training-easier/
►Sheng-Yu Wang et all, "Sketch Your Own GAN", 2021, https://arxiv.org/pdf/2108.02774v1.pdf
►Project link: https://peterwang512.github.io/GANSketching/
►Code: https://github.com/PeterWang512/GANSketching
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/