Generative Models 🎨
Deep learning is also used in generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These models generate new data that resembles existing data and are used in applications like image generation, art creation, and data augmentation.
Applications:
Image Synthesis: Generating realistic images, such as generating artwork, creating realistic human faces, or even designing new products.
Data Augmentation: Creating synthetic data to augment real datasets, useful in training AI models when real data is scarce or expensive to obtain.
Content Creation: In creative industries, deep learning models can generate new music, artwork, or written content.
How
#OpenfabricAI (OFN) Uses Deep Learning:
Collaboration and Sharing:
#OpenfabricAI allows the decentralized development of generative models, enabling creators to collaborate on generating new forms of media or synthetic data.
Real-Time Collaboration: Users can share trained generative models across the Openfabric ecosystem to help businesses or artists create unique content.