You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
pytorch-image-models/docs/models/.templates/models/ensemble-adversarial.md

3.1 KiB

# Ensemble Adversarial Inception ResNet v2

Inception-ResNet-v2 is a convolutional neural architecture that builds on the Inception family of architectures but incorporates residual connections (replacing the filter concatenation stage of the Inception architecture).

This particular model was trained for study of adversarial examples (adversarial training).

The weights from this model were ported from Tensorflow/Models.

{% include 'code_snippets.md' %}

How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

Citation

@article{DBLP:journals/corr/abs-1804-00097,
  author    = {Alexey Kurakin and
               Ian J. Goodfellow and
               Samy Bengio and
               Yinpeng Dong and
               Fangzhou Liao and
               Ming Liang and
               Tianyu Pang and
               Jun Zhu and
               Xiaolin Hu and
               Cihang Xie and
               Jianyu Wang and
               Zhishuai Zhang and
               Zhou Ren and
               Alan L. Yuille and
               Sangxia Huang and
               Yao Zhao and
               Yuzhe Zhao and
               Zhonglin Han and
               Junjiajia Long and
               Yerkebulan Berdibekov and
               Takuya Akiba and
               Seiya Tokui and
               Motoki Abe},
  title     = {Adversarial Attacks and Defences Competition},
  journal   = {CoRR},
  volume    = {abs/1804.00097},
  year      = {2018},
  url       = {http://arxiv.org/abs/1804.00097},
  archivePrefix = {arXiv},
  eprint    = {1804.00097},
  timestamp = {Thu, 31 Oct 2019 16:31:22 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1804-00097.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}