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GAN networks: generation of hyperrealistic images and videos

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 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 chinese overseas asia database 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.

Recurrent Neural Networks (RNN): Natural Language and Speech Processing

Tools: Voice assistants (Siri, Alexa), Amazon Transcribe.

How they work: These AI models process data internal analysis, the starting point for a solid strategy streams and remember precise information to make predictions and personalize responses.

Applications: Chatbots with more natural conversations, voice-to-text conversion and vice versa in virtual assistants, sentiment analysis on social networks and customer reviews.

Use cases:

  • Fashion stores like Zara and Mango use AI to create videos of their products without the need for photo shoots.
  • Sephora and other cosmetics companies ee leads exploit social media by generating tutorials and demonstrations of their products.
  • More and more coaches and artists are using tools like Pictory, FlexClip or Runway AI to promote themselves on social media while saving time and gaining engagement.
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