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LAuReL: Learned Augmented Residual Layer

Created by
  • Haebom

Author

Gaurav Menghani, Ravi Kumar, Sanjiv Kumar

Outline

In this paper, we propose a novel structure, Learned Augmented Residual Layer (LAuReL), which generalizes the existing residual connection. LAuReL aims to improve the model performance and efficiency by replacing the existing residual connection. Experimental results show that it achieves 60% of the performance improvement that can be obtained by adding additional layers on ResNet-50 and ImageNet 1K tasks, while increasing the number of parameters by only 0.003%, and achieves the same performance with 2.6x fewer parameters. In addition, when pre-training LLMs with 1 billion and 4 billion parameters, it shows performance improvements of 2.54% to 20.05% in various subtasks, while the additional parameters are only 0.012% and 0.1%, respectively. This means that it brings performance improvements on both vision and language models.

Takeaways, Limitations

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Takeaways:
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LAuReL presents a novel method to simultaneously improve model performance and efficiency by improving the existing residual connection.
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It shows performance improvements in both vision and language models, suggesting wide applicability.
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It can contribute to model lightweighting by achieving large performance improvements with a small parameter increase.
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Limitations:
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Further studies are needed to determine whether the experimental results presented in this paper can be generalized to all types of models and tasks.
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It is necessary to analyze the extent to which the performance improvement of LAuReL depends on specific hyperparameter settings.
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Further comparative analysis with other state-of-the-art residual connection improvement techniques is needed.
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