Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> 3) POOLING LAYER <s> Many modern visual recognition algorithms incorporate a step of spatial 'pooling', where the outputs of several nearby feature detectors are combined into a local or global 'bag of features', in a way that preserves task-related information while removing irrelevant details. Pooling is used to achieve invariance to image transformations, more compact representations, and better robustness to noise and clutter. Several papers have shown that the details of the pooling operation can greatly influence the performance, but studies have so far been purely empirical. In this paper, we show that the reasons underlying the performance of various pooling methods are obscured by several confounding factors, such as the link between the sample cardinality in a spatial pool and the resolution at which low-level features have been extracted. We provide a detailed theoretical analysis of max pooling and average pooling, and give extensive empirical comparisons for object recognition tasks. <s> BIB001 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> 3) POOLING LAYER <s> Full end-to-end text recognition in natural images is a challenging problem that has received much attention recently. Traditional systems in this area have relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this paper, we take a different route and combine the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a common framework to train highly-accurate text detector and character recognizer modules. Then, using only simple off-the-shelf methods, we integrate these two modules into a full end-to-end, lexicon-driven, scene text recognition system that achieves state-of-the-art performance on standard benchmarks, namely Street View Text and ICDAR 2003. <s> BIB002
The purpose of the pooling layer is to ensure the shift-invariance and lowers the computational burden by reducing the resolution of the feature maps. It is usually placed after the convolutional layer. It takes the feature map which is generated from the convolutional layer and outputs a single value for each receptive field (pooling window) according to the pooling operation. The pooling layer performs max pooling BIB001 , sum, and mean pooling BIB002 . Figure 9 shows different pooling operations. Also, there are other versions of pooling layers proposed for some tasks, such as Spatial Pyramid Pooling (SPP) BIB002 that can generates a fixed length of features regardless of the input size.