Add to ORKGInternational audienceDeep convolutional neural networks accuracy is heavily impacted by rotations of the input data. In this paper, we propose a convolutional predictor that is invariant to rotations in the input. This architecture is capable of predicting the angular orientation without angle-annotated data. Furthermore, the predictor maps continuously the random rotation of the input to a circular space of the prediction. For this purpose, we use the roto-translation properties existing in the Scattering Transform Networks with a series of 3D Convolutions. We validate the results by training with upright and randomly rotated samples. This allows further applications of this work on fields like automatic re-orientation of randomly oriente...
International audienceConvolutional Neural Network (CNNs) models’ size reduction has recently gained...
International audienceConvolutional Neural Network (CNNs) models’ size reduction has recently gained...
International audienceIn many computer vision tasks, we expect a particular behavior of the output w...
International audienceDeep convolutional neural networks accuracy is heavily impacted by rotations o...
International audienceDeep convolutional neural networks accuracy is heavily impacted by rotations o...
International audienceDeep convolutional neural networks accuracy is heavily impacted by rotations o...
42ème journée ISS FranceDeep convolutional neural networks accuracy is heavily impacted by the rotat...
42ème journée ISS FranceDeep convolutional neural networks accuracy is heavily impacted by the rotat...
42ème journée ISS FranceDeep convolutional neural networks accuracy is heavily impacted by the rotat...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
International audienceIn classification tasks, the robustness against various image transformations ...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
International audienceConvolutional Neural Network (CNNs) models’ size reduction has recently gained...
International audienceConvolutional Neural Network (CNNs) models’ size reduction has recently gained...
International audienceIn many computer vision tasks, we expect a particular behavior of the output w...
International audienceDeep convolutional neural networks accuracy is heavily impacted by rotations o...
International audienceDeep convolutional neural networks accuracy is heavily impacted by rotations o...
International audienceDeep convolutional neural networks accuracy is heavily impacted by rotations o...
42ème journée ISS FranceDeep convolutional neural networks accuracy is heavily impacted by the rotat...
42ème journée ISS FranceDeep convolutional neural networks accuracy is heavily impacted by the rotat...
42ème journée ISS FranceDeep convolutional neural networks accuracy is heavily impacted by the rotat...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
International audienceIn classification tasks, the robustness against various image transformations ...
In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated obje...
International audienceConvolutional Neural Network (CNNs) models’ size reduction has recently gained...
International audienceConvolutional Neural Network (CNNs) models’ size reduction has recently gained...
International audienceIn many computer vision tasks, we expect a particular behavior of the output w...