We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a p...

Over 15% of the data sets catalogued in the Gene Expression Omnibus Database involve RNA samples that have been pooled before hybridization. Pooling affects data quality and inference, but the exact effects are not yet known because pooling has not been systematically studied in the context of microarray experiments. Here we report on the results of an experiment designed to evaluate the utilit...

Convolutional networks almost always incorporate some form of spatial pooling, and very often it is α × α max-pooling with α = 2. Max-pooling act on the hidden layers of the network, reducing their size by an integer multiplicative factor α. The amazing by product of discarding 75% of your data is that you build into the network a degree of invariance with respect to translations and elastic di...

We introduce Eigen Evolution Pooling, an efficient method to aggregate a sequence of feature vectors. Eigen evolution pooling is designed to produce compact feature representations for a sequence of feature vectors, while maximally preserving as much information about the sequence as possible, especially the temporal evolution of the features over time. Eigen evolution pooling is a general pool...

Object recognition is difficult because the appearance of an object changes in many different ways. To recognize objects robustly, one needs representations that are constant despite those changes. Such invariant representations can be obtained by features with low sensitivity to various visual transformations. Spatial pooling is a widely used technique for extracting invariant features from im...

Convolutional autoencoders (CAEs) are unsupervised feature extractors for high-resolution images. In the preprocessing step, whitening transformation has widely been adopted to remove redundancy by making adjacent pixels less correlated. Pooling is a biologically inspired operation to reduce the resolution of feature maps and achieve spatial invariance in convolutional neural networks. Conventi...

A key step in many image quantification solutions is feature pooling, where subsets of lower-level features are combined so that higher-level, more invariant predictions can be made. The pooling region which defines the subsets often has a fixed spatial size and geometry, but data adaptive pooling regions have also been used. In this paper we investigate pooling strategies for the data adaptive...