Model Architecture

➺ Design Principles:

  • Layer composition
  • Skip connections
  • Attention mechanisms
  • Bottleneck structures 

    • ➺ Common Architectures:

    Transformer-based 
  • Multi-head attention
  • Position embeddings 
  • Layer normalization
  • Feed-forward networks 
    • CNN-based
  • Convolution layers
  • Pooling strategies
  • Residual connections
  • Feature pyramids
    • Hybrid Approaches 
  • CNN-Transformer fusion
  • Multi-modal architectures
  • Cross-attention mechanisms 

    • ➺ Scaling Strategies:

  • Depth scaling
  • Width scaling
  • Resolution scaling
  • Compound scaling 

    • ➺ Performance Considerations:

  • Receptive field analysis
  • Parameter efficiency
  • Memory bandwidth
  • Computational complexity