Training Multi-Modal Models
Train models that understand images and text together. Contrastive learning, vision-language pre-training, and alignment techniques.
TL;DR
Multi-modal training aligns vision and language representations. CLIP-style contrastive learning matches images with captions. Enables zero-shot classification, image search, and vision-language tasks.
Training approaches
Contrastive (CLIP-style):
- Pair images with captions
- Learn shared embedding space
- Images and matching text close together
Captioning (encoder-decoder):
- Vision encoder + language decoder
- Generate descriptions of images
Visual question answering:
- Combine vision and language understanding
- Answer questions about images
Data requirements
- Image-text pairs (millions needed)
- Sources: Web scraping, curated datasets
- Quality matters more than quantity
Architectures
Vision encoders: ViT, ResNet, ConvNeXT
Text encoders: BERT, GPT, T5
Fusion: Cross-attention, adapter layers
Training challenges
- Computational cost (large datasets, models)
- Alignment difficulty
- Modality imbalance
Applications
- Zero-shot image classification
- Image search
- Visual chatbots
- Content moderation
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Key Terms Used in This Guide
Training
The process of feeding data to an AI system so it learns patterns and improves its predictions over time.
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
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