Understanding Convolutional Neural Networks for Object Recognition

Since deep learning originates from the field of computer vision this talk we will focus more closely on deep learning approaches for computer vision problems. We will focus on convolutional neural networks (CNN or ConvNet), how they work, what makes them particularly useful for computer vision problems, what are the important "tricks" that makes them work that well (ReLU, dropout, batch norm ...), and what can visualization of feature tell us about CNNs. The talk will start with the basics of deep learning (gradient descent and back-propagation) so no prior knowledge is needed but some knowledge of mathematics (statistics and derivatives) could be useful for properly understanding more advanced "tricks". At the end of the talk we will also look at the method being developed at ViCoS lab in UL FRI that tries to advance CNNs by combining them with compositional hierarchies and improve the understanding of features.