GAN networks: generation of hyperrealistic images and videos

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Tools: StyleGAN (NVIDIA), DeepFake.

How they work: A “generator” creates images or videos from scratch and a “discriminator” evaluates them until they look real. This process is repeated thousands of times until the image or video is perfected.

Applications: Graphic design and digital art, video games and animation, virtual models for fashion brands.

Transformers: Text Generation and Speech Recognition
Tools: Gemini, ChatGPT, Google Translate, Grammarly.

How they work: They are large-scale language models (LLMs) that process text or new zealand phone number list speech input by analyzing the full context (not word by word). They then generate coherent responses based on patterns learned from millions of texts.

Applications: Chatbots and virtual assistants for real-time responses, content automation, translation and text correction.

Variational Autoencoders (VAEs): Creating Images and Text
Tools: TensorFlow, PyTorch.

How it works: The AI ​​system compresses and reconstructs data to create content in a more controlled and adjustable way.

Applications: Stylized versions of images, generation of detailed medical images from low resolution, detailed images from sketches.

Diffusion models: creating images with high precision
Tools: Stable Diffusion, DALL·E 2, Runway ML.

How they work: AI uses machine learning models to process an image as random noise and progressively refine it. Unlike GAN networks, it does not require a discriminator and, in many cases, can also generate realistic images.

Applications: Creation of visual concepts for product and packaging design, creation of social media ads, advanced image and video editing.

Flow-based models: creating images, audio and video
Tools: Glow, WaveGlow (NVIDIA), Suno AI.

How they work: AI transforms simple data into complex content while maintaining consistency and high quality. They are more predictable and controllable than GANs or broadcast models, which is key for brands looking for consistency across images and audio.

Applications: Creation of synthetic voices for virtual assistants and promotional videos, generation of garment variations in different colors, adaptation of visual or audio content to different audiences.