Authors:
Muhammad Ali
;
Omar Alsuwaidi
and
Salman Khan
Affiliation:
Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, U.A.E.
Keyword(s):
Global Average Pool, Flexpool, Multiscale, Regularized Flexpool, Simple Auxiliary Classifier(SAC).
Abstract:
In Computer Vision, the basic pipeline of most convolutional neural networks (CNNs) consists of multiple feature extraction processing layers, wherein the input signal is downsampled into a lower resolution in each subsequent layer. This downsampling process is commonly referred to as pooling, an essential operation in CNNs. It improves the model’s robustness against variances in transformation, reduces the number of trainable parameters, increases the receptive field size, and reduces computation time. Since pooling is a lossy process yet crucial in inferring high-level information from low-level information, we must ensure that each subsequent layer perpetuates the most prominent information from previous activations to aid the network’s discriminability. The standard way to apply this process is to use dense pooling (max or average) or strided convolutional kernels. In this paper, we propose a simple yet effective adaptive pooling method, referred to as FlexPooling, which generali
zes the concept of average pooling by learning a weighted average pooling over the activations jointly with the rest of the network. Moreover, attaching the CNN with Simple Auxiliary Classifiers (SAC) further demonstrates the superiority of our method as compared to the standard methods. Finally, we show that our simple approach consistently outperforms baseline networks on multiple popular datasets in image classification, giving us around a 1-3% increase in accuracy.
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