LambdaNetworks- Modeling Long-Range Interactions Without Attention

Explore the innovative LambdaNetworks approach to modeling long-range interactions in computer vision without traditional attention mechanisms. Dive into the technical details of lambda layers, which transform contextual information into linear functions for efficient processing of high-resolution images and long sequences. Learn how this method achieves state-of-the-art accuracy on ImageNet classification while being significantly faster than EfficientNets. Examine the framework's versatility in handling global, local, and masked contexts, and understand its implementation using standard neural network operations. Compare LambdaNetworks to convolutional and attentional counterparts in terms of performance and computational efficiency for image classification and object detection tasks.

- Introduction & Overview
- Attention Mechanism Memory Requirements
- Lambda Layers vs Attention Layers
- How Lambda Layers Work
- Attention Re-Appears in Lambda Layers
- Positional Encodings
- Extensions and Experimental Comparisons
- Code