How to break the opencv library limits in face detection | #computervision #opencv #argovision
https://www.vision-ary.net/2015/03/largest-boosted-cascades-opencv-lbp-haar-hog/
https://www.vision-ary.net/2015/03/largest-boosted-cascades-opencv-lbp-haar-hog/
Vision-ary
HAAR LBP HOG Cascades for OpenCV – updated 2021 - Vision-ary
Introduction
In the last years, Haar LBP and HOG cascades for OpenCV became popular thanks to the even more satisfying detection capabilities of a wide range of objects. The success is motivated by the efficiency and the capability of this approach to detect…
In the last years, Haar LBP and HOG cascades for OpenCV became popular thanks to the even more satisfying detection capabilities of a wide range of objects. The success is motivated by the efficiency and the capability of this approach to detect…
GANs are broken at both the computational and algorithmic levels | #DeepLearning #computervision #argovision
Paper: https://arxiv.org/abs/1705.10461
Paper: https://arxiv.org/abs/1705.10461
arXiv.org
The Numerics of GANs
In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient...
Generative 3D Shapes Using Autoencoder Networks | #deeplearning #computervision #NeuralNetworks
https://www.youtube.com/watch?time_continue=2&v=25xQs0Hs1z0
https://www.youtube.com/watch?time_continue=2&v=25xQs0Hs1z0
YouTube
Exploring Generative 3D Shapes Using Autoencoder Networks
We propose a new algorithm for converting unstructured triangle meshes into ones with a consistent topology for machine learning applications. We combine the...
Latent space interpolation on Cifar by #Nvidia research | #deeplearning #argovision #artificialintelligence #NeuralNetworks #computervision