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.
92 lines
2.9 KiB
92 lines
2.9 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](https://paperswithcode.com/method/residual-connection) (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](https://github.com/tensorflow/models).
|
|
|
|
{% include 'code_snippets.md' %}
|
|
|
|
## How do I train this model?
|
|
|
|
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
|
|
|
|
## Citation
|
|
|
|
```BibTeX
|
|
@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}
|
|
}
|
|
```
|
|
|
|
<!--
|
|
Models:
|
|
- Name: ens_adv_inception_resnet_v2
|
|
Metadata:
|
|
FLOPs: 16959133120
|
|
Training Data:
|
|
- ImageNet
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Auxiliary Classifier
|
|
- Average Pooling
|
|
- Average Pooling
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Dense Connections
|
|
- Dropout
|
|
- Inception-v3 Module
|
|
- Max Pooling
|
|
- ReLU
|
|
- Softmax
|
|
File Size: 223774238
|
|
Tasks:
|
|
- Image Classification
|
|
ID: ens_adv_inception_resnet_v2
|
|
Crop Pct: '0.897'
|
|
Image Size: '299'
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_resnet_v2.py#L351
|
|
In Collection: Ensemble Adversarial
|
|
Collections:
|
|
- Name: Ensemble Adversarial
|
|
Paper:
|
|
title: Adversarial Attacks and Defences Competition
|
|
url: https://paperswithcode.com//paper/adversarial-attacks-and-defences-competition
|
|
type: model-index
|
|
Type: model-index
|
|
-->
|