How can I implement a single-head attention mechanism for the CIFAR-10 dataset and what modifications are needed when adapting from a multi-head attention reference implementation

Question

Can you tell me how to implement a single-head attention mechanism for the CIFAR-10 dataset and what modifications are needed when adapting from a multi-head attention reference implementation?

Answer 1

To implement a single-head attention mechanism for CIFAR-10, adapt a multi-head attention model by removing multiple projection layers, using a single set of query, key, and value projections, and maintaining the scaled dot-product attention computation.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses a Single-Head Attention Layer to process image features.
• Removes Multi-Head Complexity by using a single set of query, key, and value projections.
• Applies Scaled Dot-Product Attention to focus on important image regions.
• Flattens CIFAR-10 Images before feeding into the attention mechanism.
• Uses Fully Connected Layers for final classification.

Hence, adapting a multi-head attention model to a single-head attention mechanism for CIFAR-10 requires simplifying query-key-value transformations while preserving the core attention computation for image classification.