# AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework

arXiv: https://arxiv.org/abs/2308.03321 · HTML: https://mlsanity.com/paper/2308.03321/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [Self-Challenging Improves Cross-Domain Generalization](https://mlsanity.com/paper/2007.02454/) (2020)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness](https://mlsanity.com/paper/2010.08001/) (2020)
- [Rethinking Model Ensemble in Transfer-based Adversarial Attacks](https://mlsanity.com/paper/2303.09105/) (2023)
- [Adversarially Adaptive Normalization for Single Domain Generalization](https://mlsanity.com/paper/2106.01899/) (2021)
- [Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks](https://mlsanity.com/paper/1805.07925/) (2018)
- [T-SEA: Transfer-based Self-Ensemble Attack on Object Detection](https://mlsanity.com/paper/2211.09773/) (2022)
- [Differentiable Learning-to-Normalize via Switchable Normalization](https://mlsanity.com/paper/1806.10779/) (2018)
- [Learning to Generalize across Domains on Single Test Samples](https://mlsanity.com/paper/2202.08045/) (2022)
- [Bootstrap Generalization Ability from Loss Landscape Perspective](https://mlsanity.com/paper/2209.08473/) (2022)
- [Understanding and Improving Group Normalization](https://mlsanity.com/paper/2207.01972/) (2022)

## Built on by (most cited first, 2 of 2)

- [Rethinking Centered Kernel Alignment in Knowledge Distillation](https://mlsanity.com/paper/2401.11824/) (2024)
- [Quantile Activation: Correcting a Failure Mode of ML Models](https://mlsanity.com/paper/2405.11573/) (2024)

## Abstract (arXiv)

The success of deep learning is inseparable from normalization layers. Researchers have proposed various normalization functions, and each of them has both advantages and disadvantages. In response, efforts have been made to design a unified normalization function that combines all normalization procedures and mitigates their weaknesses. We also proposed a new normalization function called Adaptive Fusion Normalization. Through experiments, we demonstrate AFN outperforms the previous normalization techniques in domain generalization and image classification tasks.