MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs

Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to pooling operations, encoder-decoder networks used for pixel-level predictions also require unpooling. It is common to combine unpooling with convolution or deconvolution for up-sampling. However, using its morphological properties, unpooling can be generalised and improved. Extensive experimentation on two tasks and three large-scale datasets shows that morphological pooling and unpooling lead to improved predictive performance at much reduced parameter counts.

[1]  T. Gevers,et al.  Geometric Back-propagation in Morphological Neural Networks. , 2023, IEEE transactions on pattern analysis and machine intelligence.

[2]  Jesus Angulo,et al.  Some Open Questions on Morphological Operators and Representations in the Deep Learning Era - A Personal Vision , 2021, DGMM.

[3]  Santiago Velasco-Forero,et al.  Scale Equivariant Neural Networks with Morphological Scale-Spaces , 2021, DGMM.

[4]  Hossein Khosravi,et al.  Pooling Methods in Deep Neural Networks, a Review , 2020, ArXiv.

[5]  Angela Yao,et al.  Deep morphological networks , 2020, Pattern Recognit..

[6]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[7]  Daniel E. Worrall,et al.  Deep Scale-spaces: Equivariance Over Scale , 2019, NeurIPS.

[8]  Jian Yang,et al.  UP-CNN: Un-pooling augmented convolutional neural network , 2017, Pattern Recognit. Lett..

[9]  Petros Maragos,et al.  Morphological Perceptrons: Geometry and Training Algorithms , 2017, ISMM.

[10]  Silvio Savarese,et al.  Joint 2D-3D-Semantic Data for Indoor Scene Understanding , 2017, ArXiv.

[11]  Vincent Dumoulin,et al.  Deconvolution and Checkerboard Artifacts , 2016 .

[12]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Nassir Navab,et al.  Deeper Depth Prediction with Fully Convolutional Residual Networks , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[18]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[20]  Thomas Brox,et al.  Learning to generate chairs with convolutional neural networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[23]  Meng Wang,et al.  Spatial Pooling of Heterogeneous Features for Image Classification , 2014, IEEE Transactions on Image Processing.

[24]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[25]  David B. Dunson,et al.  Deep Learning with Hierarchical Convolutional Factor Analysis , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[27]  Derek Hoiem,et al.  Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.

[28]  Graham W. Taylor,et al.  Adaptive deconvolutional networks for mid and high level feature learning , 2011, 2011 International Conference on Computer Vision.

[29]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[30]  Jean Ponce,et al.  Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[32]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[34]  Henk J. A. M. Heijmans,et al.  Algebraic Framework for Linear and Morphological Scale-Spaces , 2002, J. Vis. Commun. Image Represent..

[35]  Peter Sussner,et al.  Morphological perceptron learning , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[36]  Peter Sussner,et al.  An introduction to morphological neural networks , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[37]  Arnold W. M. Smeulders,et al.  The Morphological Structure of Images: The Differential Equations of Morphological Scale-Space , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Gabriela Csurka,et al.  What is a good evaluation measure for semantic segmentation? , 2013, BMVC.

[39]  Leo Dorst,et al.  Quadratic Structuring Functions in Mathematical Morphology , 1996, ISMM.

[40]  J. Serra,et al.  An overview of morphological filtering , 1992 .

[41]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[42]  Qi Tian,et al.  Ieee Transactions on Image Processing Spatial Pooling of Heterogeneous Features for Image Classification , 2022 .