Papers › LambdaNetworks: Modeling Long-Range Interactions Without Attention
LambdaNetworks: Modeling Long-Range Interactions Without Attention
Irwan Bello
We present lambda layers -- an alternative framework to self-attention -- for capturing long-range interactions between an input and structured contextual information (e.g. a pixel surrounded by other pixels). Lambda layers capture such interactions by transforming available contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Similar to linear attention, lambda layers bypass expensive attention maps, but in contrast, they model both content and position-based interactions which enables their application to large structured inputs such as images. The resulting neural network architectures, LambdaNetworks, significantly outperform their convolutional and attentional counterparts on ImageNet classification, COCO object detection and COCO instance segmentation, while being more computationally efficient. Additionally, we design LambdaResNets, a family of hybrid architectures across different scales, that considerably improves the speed-accuracy tradeoff of image classification models. LambdaResNets reach excellent accuracies on ImageNet while being 3.2 - 4.4x faster than the popular EfficientNets on modern machine learning accelerators. When training with an additional 130M pseudo-labeled images, LambdaResNets achieve up to a 9.5x speed-up over the corresponding EfficientNet checkpoints.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | LambdaResNet200 | Number of params | 42M | #327 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | LambdaResNet200 | Top 1 Accuracy | 84.3% | #327 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | LambdaResNet152 | Number of params | 35M | #366 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | LambdaResNet152 | Top 1 Accuracy | 84.0% | #366 of 1060 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Lambda Layer
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