Learn To Pay Attention (1804.02391v2)

Abstract: We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map. Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parameterised by the score matrices, must \textit{alone} be used for classification. Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values. Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter. Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets. When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset. We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.

• The paper presents a novel attention module that enhances CNN image classification by focusing on salient features via a softmax-normalized 2D score matrix.
• It integrates this mechanism into architectures like VGGNet and ResNet, yielding around a 7% accuracy improvement on the CIFAR-100 dataset and better generalization on multiple benchmarks.
• The approach improves model interpretability and fortifies robustness against adversarial attacks, making CNN decisions more transparent and reliable.

Learn to Pay Attention: Enhancing CNNs for Image Classification

The paper "Learn to Pay Attention" presents a novel method for incorporating an end-to-end-trainable attention module within standard convolutional neural networks (CNNs) to boost their performance in image classification tasks. This work tackles the interpretation problem in CNNs by enhancing the model's ability to focus on salient image regions while suppressing irrelevant content.

Methodology

The core contribution of this paper lies in the design of an attention mechanism that computes a 2D matrix of scores over spatial locations of intermediate feature maps within the CNN. Each score reflects the relevance of a particular feature for the classification task. This is achieved by implementing a normalization step using a softmax function to produce probabilistic attention maps that highlight relevant image regions.

The proposed methodology modifies standard CNN architectures, such as VGGNet and ResNet, by integrating this attention module. The CNNs are then trained under a convex combination constraint where only the attention-weighted representations of intermediary feature vectors contribute to the final classification layer. This encourages the network to learn discriminative feature patterns focused on relevant image parts.

Experimental Results

Empirical evaluations in the paper highlight significant performance improvements:

• The attention-enhanced VGG model demonstrates an accuracy gain of approximately 7% on the CIFAR-100 dataset.
• The attention maps exhibit superior generalization capabilities when evaluated across six unseen benchmark datasets, outperforming traditional CNN-derived attention methods and saliency maps in weakly supervised segmentation tasks as shown in the Object Discovery dataset.
• The model shows improved robustness against adversarial attacks, specifically the fast gradient sign method, indicating that attention not only aids classification but also fortifies the model against small perturbations in input data.

Implications

This approach empowers CNNs, traditionally criticized for their black-box nature, with a degree of transparency via meaningful visualization of what drives the network’s decisions. The attention maps offer interpretability, suggesting potential applications in areas where understanding model predictions is crucial, such as medical diagnosis.

Moreover, the attention mechanism proves beneficial in cross-domain scenarios, where trained models on one dataset can successfully classify images from different domains, illustrating enhanced model transfer capabilities.

Future Directions

In future work, exploration of attention mechanisms in different network architectures and applications could further extend these findings. Additionally, research into diverse forms of trainable compatibility functions may provide deeper insights into how to leverage different scales of attention for tasks beyond image classification, such as natural language processing or time-series forecasting.

Overall, the research presented in this paper adds significant value to the field of deep learning by demonstrating that learning to "pay attention" effectively can enhance both the interpretability and performance of CNNs in complex tasks.