What are the common types of Generative AI models?
Quality Thoughts – The Best Generative AI (Gen AI) Training Institute in Hyderabad with Live Internship by Industry Experts
Artificial Intelligence is no longer just about analyzing data—it’s now about creating. With the rise of Generative AI (Gen AI), machines are learning how to generate human-like text, images, videos, music, and even software code. This has created a massive demand for AI professionals skilled in Gen AI models and applications.
If you're a graduate, postgraduate, someone with an education gap, or looking for a career or domain change, Quality Thoughts is your ideal destination. We are proud to be recognized as the best Generative AI training institute in Hyderabad, offering a comprehensive course curriculum and a live intensive internship program under the mentorship of industry experts.
What Are the Common Types of Generative AI Models?
Generative AI models are algorithms trained on large datasets to generate new, original content. Here are the most commonly used and powerful Gen AI models you’ll come across in real-world applications:
1. Generative Adversarial Networks (GANs)
GANs consist of two neural networks — a generator and a discriminator — that compete against each other. The generator creates new data (like images), while the discriminator evaluates its authenticity. Over time, the generator learns to create highly realistic content. GANs are widely used in image generation, art creation, style transfer, and deepfake technology.
2. Variational Autoencoders (VAEs)
VAEs are deep learning models that compress data into a smaller form (encoding) and then reconstruct it (decoding). Unlike GANs, VAEs are more structured and mathematically grounded, making them useful for generating synthetic images, 3D objects, and even speech with better control over output variations.
3. Transformers (e.g., GPT, BERT, T5)
Transformers are the foundation of many modern Large Language Models (LLMs) like GPT-4, Google Gemini, and BERT. These models can understand and generate human-like text with remarkable accuracy. They are used in chatbots, content writing, code generation, translation, and summarization.
4. Diffusion Models
Diffusion models work by learning to reverse the process of noise addition to generate clear, realistic images. They are gaining popularity for producing high-quality visual content, used in tools like DALL·E 2, Midjourney, and Stable Diffusion.
5. Autoregressive Models
These models generate content one step at a time, predicting the next word, pixel, or note based on previous data. GPT-series models are a prime example. They are efficient for text generation, music composition, and code auto-completion.
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