This lecture overviews Generative Adversarial Networks that have many applications in Media Production. It covers the following topics in detail: Theoretical ML background (cross-entropy loss for binary classification), Deep fake, Generator function, Discriminator function, GANs training using Minimax optimization or Heuristic optimization. The most notable GAN architectures are presented: cGAN, IcGAN, Convolutional GANs, LSTM-GAN, TP-GAN,… Continue reading Generative Adversarial Networks
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Adversarial Machine Learning
This lecture overviews Adversarial Machine Learning that has many applications in DNN robustness and in privacy protection. It covers the following topics in detail: Adversarial Examples, Attack Methods, Adversarial Face De-Identification, Adversarial Defenses.
Deep Semantic Image Segmentation
Semantic image segmentation is a very important computer vision task with several applications in autonomous systems perception, robotic vision and medical imaging. Recent semantic image segmentation methods rely on deep neural networks and aim to assign a specific class label to each pixel of the input image. This lecture overviews the topic and addresses some… Continue reading Deep Semantic Image Segmentation
Few Shot Object Recognition
This lecture overviews Few Shot Object Recognition that has many applications in image classification, when few training data are available. It covers the following topics in detail: Few-shot Image Learning definitions and methods. Applications in sports video athlete recognition.
Special topics in Object Detection
This lecture overviews Special topics in Object Detection that has many applications in embedded computing and drone vision. It covers the following topics in detail: Embedded object detection, Small object detection, Person detection from aerial views.
Deep Object Detection
Recently, Convolutional Neural Networks (CNNs) have been used for object/target (e.g., face, person, car, pedestrian, road sign) detection with great results. However, using such CNN models on embedded processors for real-time processing is prohibited by HW constraints. In that sense, various architectures and settings will be examined in order to facilitate and accelerate the use… Continue reading Deep Object Detection