mldl / open-aff

code and trained models for "Attentional Feature Fusion"

Attentional Feature Fusion

MXNet/Gluon code for "Attentional Feature Fusion" https://arxiv.org/abs/2009.14082

What's in this repo so far:

• Code, trained models, and training logs for CIFAR-10, CIFAR-100, and ImageNet

To Do:

• ResNeXt code needs to be able to automatically choose number of attentional layers to avoid manually setting
• ResNeXt47 on CIFAR-100 needs to re-run to see whether the accuracy could be higher
• Run ImageNet experiments with a higher number of attentional layers and a deeper backbone
• Rename the function name, e.g., ResNet110V2ASKC => ResNet20V2ASKC

Requirements

Install MXNet and Gluon-CV:

pip install --upgrade mxnet-cu100 gluoncv

Experiments

All trained model params and training logs are in ./params

The training commands / shell scripts are in cmd_scripts.txt

CIFAR-100

Architecture	Params	Accuracy
Attention-Augmented-Wide-ResNet-28-10 [3]	36.2M	81.6
SENet-29 [4]	35.0M	82.2
SKNet-29 [7]	27.7M	82.7
PyramidNet-272-alpha-200 [8]	26.0M	83.6
Neural Architecture Transfer (NAT-M4) [9]	9.0M	88.3
AutoAugment+PyramidNet+ShakeDrop [10]	26.0M	89.3
AFF-ResNet-32 (ours)	5.0M	89.3
AFF-ResNeXt-38-32x4d (ours)	7.8M	90.3

ImageNet

Architecture	Params	top-1 err.
ResNet-101 [1]	42.5M	23.2
Efficient-Channel-Attention-Net-101 [2]	42.5M	21.4
Attention-Augmented-ResNet-101 [3]	45.4M	21.3
SENet-101 [4]	49.4M	20.9
Gather-Excite-$\theta^{+}$-ResNet-101 [5]	58.4M	20.7
Local-Importance-Pooling-ResNet-101 [6]	42.9M	20.7
AFF-ResNet-50 (ours)	30.3M	20.9
AFF-ResNeXt-50-32x4d (ours)	29.9M	20.8
iAFF-ResNet-50 (ours)	35.1M	20.4
iAFF-ResNeXt-50-32x4d (ours)	34.7M	20.2

References

[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Deep Residual Learning for Image Recognition. CVPR 2016: 770-778

[2] Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, Qinghua Hu: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. CVPR 2020: 11531-11539

[3] Irwan Bello, Barret Zoph, Quoc Le, Ashish Vaswani, Jonathon Shlens: Attention Augmented Convolutional Networks. ICCV 2019: 3285-3294

[4] Jie Hu, Li Shen, Gang Sun: Squeeze-and-Excitation Networks. CVPR 2018: 7132-7141

[5] Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi: Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks. NeurIPS 2018: 9423-9433

[6] Ziteng Gao, Limin Wang, Gangshan Wu: LIP: Local Importance-Based Pooling. ICCV 2019: 3354-3363

[7] Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang: Selective Kernel Networks. CVPR 2019: 510-519

[8] Dongyoon Han, Jiwhan Kim, Junmo Kim: Deep Pyramidal Residual Networks. CVPR 2017: 6307-6315

[9] Zhichao Lu, Gautam Sreekumar, Erik D. Goodman, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti: Neural Architecture Transfer. CoRR abs/2005.05859 (2020)

[10] Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le: AutoAugment: Learning Augmentation Strategies From Data. CVPR 2019: 113-123