title: Why rotation-invariant neural networks are not used in winners of the popular competitions?
body: <p>As known, modern most popular CNN (convolutional neural network): VGG/ResNet (FasterRCNN), SSD, Yolo, Yolo v2, DenseBox, DetectNet - are not rotate invariant: <a href="https://stackoverflow.com/questions/40952163/are-modern-cnn-convolutional-neural-network-as-detectnet-rotate-invariant">Are modern CNN (convolutional neural network) as DetectNet rotate invariant?</a></p>
  
  <p>Also known, that there are several neural networks with rotate-invariance object detection:</p>
  
  <ol>
  <li><p>Rotation-Invariant Neoperceptron 2006 (<a href="https://www.researchgate.net/profile/Beat_Fasel/publication/224649475_Rotation-Invariant_Neoperceptron/links/02e7e53859a00a588b000000.pdf" rel="noreferrer">PDF</a>): <a href="https://www.researchgate.net/publication/224649475_Rotation-Invariant_Neoperceptron" rel="noreferrer">https://www.researchgate.net/publication/224649475_Rotation-Invariant_Neoperceptron</a></p></li>
  <li><p>Learning rotation invariant convolutional filters for texture classification 2016 (<a href="https://arxiv.org/pdf/1604.06720v2" rel="noreferrer">PDF</a>): <a href="https://arxiv.org/abs/1604.06720" rel="noreferrer">https://arxiv.org/abs/1604.06720</a></p></li>
  <li><p>RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection 2016 (<a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cheng_RIFD-CNN_Rotation-Invariant_and_CVPR_2016_paper.pdf" rel="noreferrer">PDF</a>): <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Cheng_RIFD-CNN_Rotation-Invariant_and_CVPR_2016_paper.html" rel="noreferrer">http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Cheng_RIFD-CNN_Rotation-Invariant_and_CVPR_2016_paper.html</a></p></li>
  <li><p>Encoded Invariance in Convolutional Neural Networks 2014 (<a href="http://theorycenter.cs.uchicago.edu/REU/2014/final-papers/sauder.pdf" rel="noreferrer">PDF</a>)</p></li>
  <li><p>Rotation-invariant convolutional neural networks for galaxy morphology prediction (<a href="https://arxiv.org/pdf/1503.07077v1" rel="noreferrer">PDF</a>): <a href="https://arxiv.org/abs/1503.07077" rel="noreferrer">https://arxiv.org/abs/1503.07077</a></p></li>
  <li><p>Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images 2016: <a href="http://ieeexplore.ieee.org/document/7560644/" rel="noreferrer">http://ieeexplore.ieee.org/document/7560644/</a></p></li>
  </ol>
  
  <p>We know, that in such image-detection competitions as: IMAGE-NET, MSCOCO, PASCAL VOC - used networks ensembles (simultaneously some neural networks). Or networks ensembles in single net such as ResNet (<a href="https://arxiv.org/abs/1605.06431" rel="noreferrer">Residual Networks Behave Like Ensembles of Relatively Shallow Networks</a>)</p>
  
  <p>But are used rotation invariant network ensembles in winners like as MSRA, and if not, then why? Why in ensemble the additional rotation-invariant network does not add accuracy to detect certain objects such as aircraft objects - which images is done at a different angles of rotation? </p>
  
  <p>It can be:</p>
  
  <ul>
  <li><p>aircraft objects which are photographed from the ground 
  <a href="https://i.stack.imgur.com/s9C7w.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/s9C7w.jpg" alt="enter image description here"></a></p></li>
  <li><p>or ground objects which are photographed from the air
  <a href="https://i.stack.imgur.com/ICqUL.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/ICqUL.jpg" alt="enter image description here"></a></p></li>
  </ul>
  
  <p>Why rotation-invariant neural networks are not used in winners of the popular object-detection competitions?</p>