An A.I.-Generated Picture Won an Art Prize. Artists Arenā€™t Happy?

Apr 11, 2024

When I first heard this news in 2022, I remembered that the world but mostly the artists went crazy.

Why?

Well, I mean think about it, you are an artist and you spent many months trying to perfect the ultimate piece. Meanwhile, one guy comes long with an AI that uses just one prompt to generate the award-winning masterpiece in just seconds.

Okay maybe it took him a couple of prompts to get it just right, but that's not the point.

The point is that other artists feel just like anyone else who feels like AI is stealing their jobs.

Whether this is true or not doesn't matter, it's what you decide to do now. You either beat them or join the trend.

I would join them, because AI is becoming more powerful by the day!

So What is the trend now?

If you asked me what the trend is now, I would say that it is generative AI. As broad as that sounds, we have covered multimodal AI being able to generate text, sound, images, and eventually video (with SORA).

But in art, what is the underlying technology that started the whole image generation craze that we see today?

Meet Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) work like an artist and critic pair. The artist creates paintings and the critic judges them. They keep learning from each other until the artist's fakes are indistinguishable from real paintings.

In AI terms, the artist is a program making new images, and the critic is another program deciding if they're real or fake, improving until the creations are incredibly lifelike.

There's one thing that I want you to know about GANs...

It's not only used for Image Generation

If you are already using tools like DALLE-2, Stable Diffusion MidJourney, etc, you are probably quite comfortable thinking that AI can just be used for image generation.

But the use of GANs spans well beyond that. Let me tell you about 9 ways in which you can use GANs:

  1. Style Transfer: Adapting one image's style to another's content.
  2. Image-to-Image Translation: Transforming images across different domains, like day to night.
  3. Photo Inpainting: Fill in missing parts of images for repair or object removal.
  4. Face Aging: Simulating a person's appearance across different ages.
  5. 3D Object Generation: Creating 3D models from 2D images.
  6. Text-to-Image Synthesis: Producing images from written descriptions.
  7. Creating Virtual Environments: Generating realistic landscapes for VR.
  8. Data Anonymization: Altering personal data in images for privacy.
  9. Medical Imaging: Enhancing and generating medical images for analysis.

But here is the best part!

We teach most of these from first principles on how to develop these projects inside our Generative Adversarial Networks (GAN) Pro course.

Here's what you will learn in this course:

  • Module 1: Introduction and Overview of GANs
  • Module 2: Vanilla GAN Training and Basic Building Blocks
  • Module 3: Implementation of Vanilla GAN
  • Module 4: Challenges and Basic Building Blocks of GANs
  • Module 5: Application of GAN with Implementation
    • 5.1 Image-to-Image Translation Using CycleGAN
    • 5.2 Text-to-Image Translation using ClipBigGAN
    • 5.3 Interactive Image Generation using Interactive GAN
    • 5.4 Photos to Animations(Emojis) using ArcaneGAN
    • 5.5 Enhancing The Resolution of an Image using ESRGAN
    • 5.6 Image Generation using StyleGAN3
  • Module 6: AI-based Image Generation
    • 6.1 Introduction to AI-Based Image Generation
    • 6.2 AI-Based Image Generators - Midjourney
    • 6.3 AI-Based Image Generators - DALL-E 2
    • 6.4 AI-Based Image Generators – Imagen
    • 6.5 AI-Based Image Generators - Stable Diffusion

As a bonus, I'll even show you how I trained a model of myself to dress me up as Ironman like the image above.

Learn more here

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  10. Generative Adversarial Networks (GANs) Course - Imagining New Realities with GANs: Generative Adversarial Networks
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