Foundation Model

FOUNDATION MODEL is a versatile machine learning model that has been pre-trained on a vast amount of unlabelled, and self-supervised data. This model can be adapted and can be fine-tuned for a wide variety of applications and tasks, giving it significant generality and adaptability.

Several Types of Foundation Models include -

1. Language Models –

• These models are pre-trained on textual data.
• Applications: text generation, translation, text summarization, chatbots, and question answering.
• Examples: GPT, BERT, T5

2. Vision Models –

• These models are pre-trained on large image datasets.
• Applications: image classification, object detection, image segmentation, and other computer vision tasks.
• Example: Vision Transformer (ViT), Residual Networks (ResNet)

3. Multi-modal Models –

• These models are pre-trained on datasets that include both text and images.
• Applications: generating images from textual descriptions, and understanding the content of images in the context of accompanying text.
• Examples: Vision-Language Transformer (VL-T5), Contrastive Language-Image Pre-training (CLIP), DALL-E

4. Speech Models –

• These models are pre-trained on audio data.
• Applications: speech recognition, speech synthesis, and other audio-related tasks.
• Examples: Wave2Vec, Whisper

5. Generative Models –

• These models are designed to generate new data samples that are similar to the training data.
• Applications: image generation, music composition, and more.
• Examples: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models

6. Graph Models –

• These models are pre-trained on graph-structured data.
• Applications: node classification, link prediction, and graph generation.
• Examples: GraphSAGE, Graph Convolutional Networks (CGN)

Finally

Hopefully, you enjoyed reading it. This was an introduction to Foundation Models. Stay tuned for our next blog, where we’ll dive into the fascinating world of Large Language Models (LLMs).

For any queries, contact me on LinkedIn.