Global Learnable Pooling with Enhancing Distinctive Features for Image Classification

Global pooling is a technique used in deep learning models to combine features from different parts of an image. However, it has some limitations that can affect the model’s performance. This survey aims to provide a comprehensive overview of strategies proposed to improve global pooling and mitigate its drawbacks.

Limitations of Global Pooling

Global pooling collapses the information from an entire feature map into a single vector, losing fine-grained spatial details. This can hinder the model’s ability to capture complex spatial relationships and local variations in the image. Moreover, uninformative features may dominate the global summary statistic, leading to poor performance.

Strategies to Improve Global Pooling

Several approaches have been proposed to address these limitations:

1. Non-linear pooling functions: Using non-linear functions like log-average-exp (LAE) or log-sum-exp (LSE) can help extract features more effectively. These functions introduce a trade-off between global average pooling and global max pooling, allowing the model to capture both local and global information.
2. Pyramid Pooling: Pyramid pooling methods divide the image into multiple regions of different scales and extract features from each region independently. This allows the model to capture multi-scale information and preserve spatial details.
3. Attention Mechanisms: Integrating attention mechanisms with global pooling can improve its discriminative power by selectively emphasizing salient regions or features while suppressing irrelevant ones.
4. Context-Aware Pooling: Context-aware pooling (CAP) is a technique that adapts the pooling function based on the context of the feature map. This allows the model to capture spatial information more effectively and improve performance.

Conclusion

In conclusion, global pooling is an essential component in deep learning models for image recognition. However, its limitations can affect the model’s performance. Various strategies have been proposed to enhance global pooling and mitigate its drawbacks, including non-linear pooling functions, pyramid pooling, attention mechanisms, and context-aware pooling. By adopting these strategies, researchers can improve the performance of deep learning models in image recognition tasks.