The pooling operation used in convolutional neural networks is a big mistake and the fact that it works so well is a disaster. - G.Hinton

Name Pooling aka Subsampling (Probably remove and move to Activation function)






Known Uses

Related Patterns

Pooling performs the same kind of space folding mechanism as activation function.



Signal Recovery from Pooling Representations

Pooling layers can be inverted with phase recovery algorithms. The SimNet architecture consists of two operators – a “similarity” operator that generalizes the inner-product operator found in ConvNets, and a soft max-average-min operator called MEX that replaces the ConvNet ReLU activation and max/average pooling layers. Inductive Bias of Deep Convolutional Networks through Pooling Geometry

Our formal understanding of the inductive bias that drives the success of convolutional networks on computer vision tasks is limited. In particular, it is unclear what makes hypotheses spaces born from convolution and pooling operations so suitable for natural images. In this paper we study the ability of convolutional networks to model correlations among regions of their input. We theoretically analyze convolutional arithmetic circuits, and empirically validate our findings on other types of convolutional networks as well. Correlations are formalized through the notion of separation rank, which for a given partition of the input, measures how far a function is from being separable. We show that a polynomially sized deep network supports exponentially high separation ranks for certain input partitions, while being limited to polynomial separation ranks for others. The network's pooling geometry effectively determines which input partitions are favored, thus serves as a means for controlling the inductive bias. Contiguous pooling windows as commonly employed in practice favor interleaved partitions over coarse ones, orienting the inductive bias towards the statistics of natural images. Other pooling schemes lead to different preferences, and this allows tailoring the network to data that departs from the usual domain of natural imagery. In addition to analyzing deep networks, we show that shallow ones support only linear separation ranks, and by this gain insight into the benefit of functions brought forth by depth - they are able to efficiently model strong correlation under favored partitions of the input. RefineNet: Multi-Path Refinement Networks with Identity Mappings for High-Resolution Semantic Segmentation

Repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.

Chained residual pooling. The output feature map then goes through the chained residual pooling block. The proposed chained residual pooling aims to capture background context from a large image region. It is able to efficiently pool features with multiple window sizes and fuse them together using learnable weights. In particular, this component is built as a chain of multiple pooling blocks, each consisting of one max-pooling layer and one convolution layer. One pooling block takes the output of the previous pooling block as input. Therefore, the current pooling block is able to re-use the result from the previous pooling operation and thus access the features from a large region without using a large pooling window. If not further specified, we use two pooling blocks each with stride 1 in our experiments. The output feature maps of all pooling blocks are fused together with the input feature map through summation of residual connections. Note that, our choice to employ residual connections also persists in this building block, which once again facilitates gradient propagation during training. In one pooling block, each pooling operation is followed by convolutions which serve as a weighting layer for the summation fusion. It is expected that this convolution layer will learn to accommodate the importance of the pooling block during the training process. Fractional Max Pooling

Convolutional networks almost always incorporate some form of spatial pooling, and very often it is alpha times alpha max-pooling with alpha=2. Max-pooling act on the hidden layers of the network, reducing their size by an integer multiplicative factor alpha. 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 distortions. However, if you simply alternate convolutional layers with max-pooling layers, performance is limited due to the rapid reduction in spatial size, and the disjoint nature of the pooling regions. We have formulated a fractional version of max-pooling where alpha is allowed to take non-integer values. Our version of max-pooling is stochastic as there are lots of different ways of constructing suitable pooling regions. We find that our form of fractional max-pooling reduces overfitting on a variety of datasets: for instance, we improve on the state-of-the art for CIFAR-100 without even using dropout. Detail-Preserving Pooling in Deep Networks

. Inspired by the human visual system, which focuses on local spatial changes, we propose detail-preserving pooling (DPP), an adaptive pooling method that magnifies spatial changes and preserves important structural detail. Importantly, its parameters can be learned jointly with the rest of the network. We analyze some of its theoretical properties and show its empirical benefits on several datasets and networks, where DPP consistently outperforms previous pooling approaches. Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs

(1) Deformation invariance is not a binary property, but rather that different tasks require different degrees of deformation stability at different layers. (2) Deformation stability is not a fixed property of a network and is heavily adjusted over the course of training, largely through the smoothness of the convolutional filters. (3) Interleaved pooling layers are neither necessary nor sufficient for achieving the optimal form of deformation stability for natural image classification. (4) Pooling confers too much deformation stability for image classification at initialization, and during training, networks have to learn to counteract this inductive bias.