# flc_pooling

**Repository Path**: whitezwh/flc_pooling

## Basic Information

- **Project Name**: flc_pooling
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-08
- **Last Updated**: 2025-02-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FrequencyLowCut Pooling 
Code for [FrequencyLowCut Pooling - Plug & Play against Catastrophic Overfitting](https://link.springer.com/chapter/10.1007/978-3-031-19781-9_3) and [Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling](https://arxiv.org/abs/2307.09804)

We provide our FrequencyLowCut (FLC) module and our Aliasing and Sinc Artifact free Pooling (ASAP) as well as examples how to implement them into common CNN structures.

The code for adversarial training used in our paper can be found [here](https://github.com/locuslab/fast_adversarial).


## Citation

Would you like to reference our **`FLC Pooling`** and **`ASAP`**? 

Then consider citing our [paper](https://link.springer.com/chapter/10.1007/978-3-031-19781-9_3) and [paper](https://arxiv.org/abs/2307.09804):


```bibtex
@inproceedings{grabinski2022frequencylowcut,
  title     = {FrequencyLowCut Pooling--Plug \& Play against Catastrophic Overfitting},
  author    = {Grabinski, Julia and Jung, Steffen and Keuper, Janis and Keuper, Margret},
  booktitle = {European Conference on Computer Vision},
  year      = {2022},
  url       = {https://arxiv.org/abs/2204.00491}
}

@article{grabinski2023fix,
  title    = {Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling},
  author   = {Grabinski, Julia and Keuper, Janis and Keuper, Margret},
  journal  = {arXiv preprint arXiv:2307.09804},
  year     = {2023}
}
```