On Global Feature Pooling for Fine-grained Visual Categorization
 Global feature pooling is a modern variant of feature pooling providing better interpretatability and regularization.Although alternative pooling methods exist, the averaging operation is still the dominating global pooling scheme in popular models.As fine-grained recognition requires learning subtle, discriminative features, we consider the question: is average pooling the optimal strategy?, "We first ask: is there a difference between features learned by global average and max pooling? Visualization and quantitative analysis show that max pooling encourages learning features of different spatial scales.", "We then ask is there a single global feature pooling variant thats most suitable for fine-grained recognition? A thorough evaluation of nine representative pooling algorithms finds that: max pooling outperforms average pooling consistently across models, datasets, and image resolutions; it does so by reducing the generalization gap; and generalized poolings performance increases almost monotonically as it changes from average to max.", "We finally ask: whats the best way to combine two heterogeneous pooling schemes? Common strategies struggle because of potential gradient conflict but the freeze-and-train trick works best.We also find that post-global batch normalization helps with faster convergence and improves model performance consistently.
A benchmark of nine representative global pooling schemes reveals some interesting findings.For fine-grained classification tasks, this paper validated that maxpooling would encourage sparser feature maps than and outperform avgpooling. 