Residual Attention Network for Image Classification
Citations: 1,399 | Paper Published: 01 Jul 2017 | Institutions: SenseTime, Tsinghua University, The Chinese University of Hong Kong, Beijing University of Posts and Telecommunications4 | Authors: Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang
The paper proposes a Residual Attention Network, which is a convolutional neural network that uses an attention mechanism to improve object recognition performance. The proposed model achieves state-of-the-art results on three benchmark datasets and can be easily scaled up to hundreds of layers using attention residual learning.
Contributions
- The proposal of Residual Attention Network, a convolutional neural network that adopts mixed attention mechanism in "very deep" structure.
- A stacked network structure is used to construct the proposed model by stacking multiple Attention Modules. This allows for mimicking bottom-up fast feedforward process and top-down attention feedback in a single feedforward process which enables end-to-end trainable networks with top-down attention.
- Extensive analyses on CIFAR-10 and CIFAR 100 datasets were conducted to verify the effectiveness of every module mentioned above.
- Attention residual learning was introduced as an effective way to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers while maintaining high accuracy levels.
- The experiment also demonstrates that our network is robust against noisy labels
Practical Implications
- The proposed Residual Attention Network can be used for object recognition tasks in various fields such as computer vision, robotics and autonomous driving.
- The attention mechanism incorporated into the network allows it to focus on important features while ignoring irrelevant ones. This makes the model more efficient and accurate than traditional convolutional neural networks.
- Since the proposed method is robust against noisy labels, it could potentially reduce human annotation efforts required during training data preparation process which would save time and resources.
Methods
- The proposal of Residual Attention Network, a convolutional neural network that adopts mixed attention mechanism.
- A stacked network structure is used to construct the proposed model by stacking multiple Attention Modules. This allows for mimicking bottom-up fast feedforward process and top-down attention feedback in a single feedforward process which enables end-to-end trainable networks with top-down attention.
- Extensive analyses on CIFAR-10 and CIFAR 100 datasets were conducted to verify the effectiveness of every module mentioned above. -Attention residual learning was introduced as an effective way to train very deep Residual Attention Networks which can be easily scaled up while maintaining high accuracy levels. -The experiment also demonstrates that our network is robust against noisy labels
Data
- CIFAR-10: A dataset consisting of 60,000 images in 10 classes (6,000 per class) for object recognition.
- CIFAR-100: Similar to CIFAR-10 but with a total number of 100 classes and each containing only 600 samples.
- ImageNet: One million training images from over one thousand categories.
Results
The proposed Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error).
It is worth noting that the method achieved 0.6% improvement in top-1 accuracy with only 46\% trunk depth and 69\% forward FLOPs compared to ResNet200.
Limitations
- The proposed Residual Attention Network may require more computational resources than traditional convolutional neural networks due to its deep structure and attention mechanism.
- Although the model is robust against noisy labels, it might still suffer from other types of data imperfections such as class imbalance or domain shift which can affect performance in real-world scenarios.