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ICLR 2021 Submission | ‘Lambda Networks’ Achieve SOTA Accuracy, Save Massive Memory

ICLR 2021 submission proposes LambdaNetworks, a transformer-specific method that reduces costs of modeling long-range interactions for CV and other applications.

Transformer-like neural network architectures have played a game-changing role in natural language processing (NLP), and are now increasingly being applied in the field of Computer Vision (CV) and related research. A paper submitted to ICLR 2021 proposes LambdaNetworks, a new transformer-specific method designed to solve the challenge of expensive attention maps in modeling long-range interactions.

In machine learning, attention mechanisms are a standard method for capturing long-range interactions in data. However, using attention on long-sequence inputs is difficult due to its huge quadratic memory footprint. For example, 32 GB of memory is required to apply a single multi-head attention layer to a batch of 256 of 64×64 input images with 8 heads, which is excessive in practice.

The paper LambdaNetworks: Modeling Long-Range Interactions Without Attention proposes a novel concept called “lambda layers,” a class of layers that provides a general framework for capturing long-range interactions between an input and a structured set of context elements. The paper also introduces “LambdaResNets”, a family architecture based on the layers that reaches SOTA accuracies on ImageNet, and is approximately 4.5x faster than the popular modern machine learning accelerator EfficientNets.

A transformer-like architecture, lambda layers transform available contexts into single linear functions (lambdas), which are then applied to each input separately.

While typical attention mechanisms define a similarity kernel between the input and context elements, lambda layers instead summarize contextual information into a fixed-size linear function, thus avoiding the prohibitive memory requirements. This suggests the applicability of lambda layers for dealing with long sequences or high-resolution images.

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In experiments, the research group tested the lambda layers and attention mechanisms on ImageNet classification with a ResNet50 architecture, with the lambda layers showing a strong advantage with just a fraction of the parameter cost.

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The lambda layers also deliver better results in both accuracy and memory-efficiency than self-attention alternatives.

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The proposed LambdaResNets family of architectures meanwhile were shown to significantly improve the speed-accuracy tradeoff of image classification models. LambdaResNets performed better on both depth and image scale than the popular EfficientNets, and achieved state-of-art performance on ImageNet accuracy.

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The paper LambdaNetworks: Modeling Long-Range Interactions Without Attention is currently under double-blind review by ICLR 2021 and is available on OpenReview. The PyTorch code can be found on the project GitHub.


Analyst: Victor Lu | Editor: Michael Sarazen; Fangyu Cai


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1 comment on “ICLR 2021 Submission | ‘Lambda Networks’ Achieve SOTA Accuracy, Save Massive Memory

  1. Pingback: [R] ‘Lambda Networks’ Achieve SOTA Accuracy, Save Massive Memory – tensor.io

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