Convolutional Neural Networks and Image Processing II

Course structure

• Image data preprocessing (number of channels, aspect ratio, image scaling, normalization of image data)
• Data augmentation (scaling, rotation, shifting, mirroring)
• Practical example of preprocessing and augmentation
• DNN architectures for Image Processing (convolution, deconvolution, pooling, residual) connections
• Advanced image classification (ImageNet dataset, VGG, ResNet, Inception, MobileNet, Efficient net)
• Practical example of transfer learning using different base models
• Image Segmentation (U-net, Object detection)
• Practical example of image segmentation and object detection
• Generative Adversarial Networks (generative networks, sampling, the adversarial model)
• Practical example of image generation
• Image Noise Reduction
• Practical example of noise reduction using U-net and GANs
• Superresolution (Upsampling, practical example of using GANs for superresolution)
• GAN enhancement of datasets
• Deep fake (Conditional GANs, examples of deep fake techniques)
• Adversarial attacks (image modification, adversarial patch, black box-attacks, cybersecurity)