Hello people,
I have done some courses on Generative AI using LLM where I got to know many methods using Large language models.
Now my question is what are the methods in GenAI using Images.
If someone could help, that means a lot to me.
Hello @Aravind.S ,
Generative AI methods for images include:
- Variational Autoencoders (VAEs): Learn a low-dimensional representation of images and generate new images from the learned latent space.
- Generative Adversarial Networks (GANs): Generate realistic images using a generator and discriminator network trained in a competitive manner.
- Conditional GANs: Condition the generator on additional information for image generation, like class labels or textual descriptions.
- Style Transfer: Transfer artistic styles from one image to another by separating style and content features.
- Autoencoders: Encode and reconstruct input images, with the decoder part used for generating new images.
- PixelRNN and PixelCNN: Generate images pixel by pixel, modeling the conditional distribution of each pixel based on previous ones.
- DeepDream: Enhance images by amplifying certain patterns using neural network activations.
- Neural Style Transfer: Combine the content of one image with the style of another to create unique visuals.
- CycleGAN: Learn mappings between two image domains without paired data, enabling image style transfer.
- Super-Resolution: Generate high-resolution images from low-resolution inputs using deep learning models.
These methods have revolutionized image generation, manipulation, and transformation in the field of Generative AI.
Hope this helps
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Thank you so much aryan
Much appreciate your help 
No problem glad to help .