Multipartite Pooling for Deep Convolutional Neural Networks
This work addresses the challenge of feature selection in deep learning for computer vision, offering an incremental improvement over existing pooling methods like max or average pooling.
The paper tackles the problem of selecting informative features in deep convolutional neural networks by proposing a novel pooling strategy that adaptively ranks features based on their discriminative power, resulting in improved generalization and performance across various benchmarks.
We propose a novel pooling strategy that learns how to adaptively rank deep convolutional features for selecting more informative representations. To this end, we exploit discriminative analysis to project the features onto a space spanned by the number of classes in the dataset under study. This maps the notion of labels in the feature space into instances in the projected space. We employ these projected distances as a measure to rank the existing features with respect to their specific discriminant power for each individual class. We then apply multipartite ranking to score the separability of the instances and aggregate one-versus-all scores to compute an overall distinction score for each feature. For the pooling, we pick features with the highest scores in a pooling window instead of maximum, average or stochastic random assignments. Our experiments on various benchmarks confirm that the proposed strategy of multipartite pooling is highly beneficial to consistently improve the performance of deep convolutional networks via better generalization of the trained models for the test-time data.