Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification

31 July 2015

Papers citing "Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification"

10 / 10 papers shown

Title
Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks F. Quiroga Franco Ronchetti Laura Lanzarini A. F. Bariviera 27 34 0 12 Oct 2023
Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales Ylva Jansson T. Lindeberg 13 23 0 11 Jun 2021
LGN-CNN: a biologically inspired CNN architecture F. Bertoni G. Citti A. Sarti 22 22 0 14 Nov 2019
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks Xiuyuan Cheng Qiang Qiu Robert Calderbank Guillermo Sapiro 30 43 0 17 May 2018
Deep Rotation Equivariant Network Junying Li Zichen Yang Haifeng Liu Deng Cai 26 59 0 24 May 2017
Oriented Response Networks Yanzhao Zhou QiXiang Ye Qiang Qiu Jianbin Jiao 27 259 0 07 Jan 2017
Learning rotation invariant convolutional filters for texture classification Diego Marcos Michele Volpi D. Tuia 34 148 0 22 Apr 2016
Exploiting Cyclic Symmetry in Convolutional Neural Networks Sander Dieleman J. Fauw Koray Kavukcuoglu 38 364 0 08 Feb 2016
Gradual DropIn of Layers to Train Very Deep Neural Networks L. Smith Emily M. Hand T. Doster AI4CE 37 33 0 22 Nov 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 268 7,640 0 03 Jul 2012