Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer (1612.03928v3)

Abstract: Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to significantly improve the performance of a student CNN network by forcing it to mimic the attention maps of a powerful teacher network. To that end, we propose several novel methods of transferring attention, showing consistent improvement across a variety of datasets and convolutional neural network architectures. Code and models for our experiments are available at https://github.com/szagoruyko/attention-transfer

• The paper demonstrates that attention transfer, by aligning teacher and student CNN attention maps, significantly reduces classification error rates.
• It introduces a combined loss function that merges cross-entropy with attention map alignment to improve network performance.
• The study underscores the potential of attention transfer to build efficient CNNs and encourages future work in object detection and knowledge distillation.

Improving CNN Performance via Attention Transfer

The paper "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer" by Sergey Zagoruyko and Nikos Komodakis addresses the enhancement of Convolutional Neural Networks (CNNs) through an innovative method known as attention transfer. This research is pivotal in the domain of neural networks, particularly for tasks involving significant visual detail such as computer vision.

Attention Mechanisms and Their Role

Attention mechanisms, which have been increasingly integrated into artificial neural networks, mimic the human cognitive ability to focus on certain parts of the visual field. These mechanisms play a critical role in various domains, including NLP and computer vision, by allowing models to selectively process important parts of the input data.

Defining Attention in CNNs

In this paper, attention is methodologically defined in the context of CNNs via spatial attention maps. These maps indicate the areas of an input image that the network emphasizes when making decisions. Two types of spatial attention maps are proposed:

1. Activation-based Attention Maps: Derived from the absolute values of activations across channels.
2. Gradient-based Attention Maps: Based on the gradients w.r.t. the input, capturing the sensitivity of the network outputs to the input changes.

Methodology of Attention Transfer

The central hypothesis of the paper is that a "teacher" CNN can improve the performance of a "student" CNN by transferring its attention maps. The idea is to train the student network not only to match the teacher's output but also to align its attention maps with those of the teacher. This process involves placing attention transfer losses at various layers of the network.

The loss function for attention transfer combines the typical cross-entropy loss with an additional term that enforces similarity between the normalized attention maps of the teacher and student networks.

Experimental Insights

CIFAR-10 Dataset

The paper conducted extensive experiments on the CIFAR-10 dataset using several network architectures, including Wide Residual Networks (WRNs) and Network-In-Network (NIN) models. The findings indicate notable improvements in performance when attention transfer is applied. For instance, when transferring attention from WRN-16-2 to WRN-16-1, the error rate dropped from 8.77 to 7.93%.

Large-Scale Datasets

Further experiments on larger datasets, such as ImageNet, demonstrated that attention transfer could also yield substantial benefits. A noteworthy case is the use of ResNet-18 as the student and ResNet-34 as the teacher, which led to a 1.1% improvement in top-1 validation accuracy.

Implications and Future Work

From a practical perspective, attention transfer offers a powerful approach to enhance smaller, less powerful CNNs by leveraging the expertise of larger models. Theoretically, this method underscores the importance of interpretability in neural networks, advocating for a deeper focus on how networks process information at different stages.

Future developments in this area could include:

• Exploration in Object Detection and Localization: Given that attention is inherently spatial, extending this work to tasks where spatial reasoning is critical could reveal further benefits.
• Integration with Knowledge Distillation: The combination of attention transfer with other knowledge transfer techniques, such as knowledge distillation, could result in more robust models.
• Architectural Innovations: Designing new network architectures that inherently support more effective attention mechanisms.

Conclusion

The insights presented in this paper elucidate the significance of attention mechanisms in CNNs and propose a novel method for performance enhancement through attention transfer. This contribution is valuable for researchers developing more efficient and effective neural network models, particularly in the field of computer vision.