Course Details

Course details of EE5179W
Course NoEE5179W
Course TitleDeep Learning for Imaging
Course Content1. Basic Neural Network:Perceptron; Multi-layer Perceptron; Back propagation; Stochastic gradient descent; Universal approximation theorem; Applications in imaging such as for denoising.2. Convolutional Neural Networks (CNN):CNN Architecture (Convolutional layer, Pooling layer, ReLu layer, fully connected layer, loss layer); Regularization methods such as dropout; Fine-tuning; Understanding and Visualizing CNN; Applications of CNN in imaging such as object/scene recognition.3. Autoencoders:Autoencoder; Denoising auto-encoder; Sparse auto-encoder; Variational autoencoder; Applications in imaging such as segnet and image generation.4. Recurrent Neural Network (RNN):Basic RNN; Long Short Term Memory (LSTM) and GRUs; Encoder-Decoder models;Applications in imaging such as activity recognition, image captioning.5. Deep Generative Models:Restricted Boltzmann machine; Deep Boltzmann machine; Recurrent Image Density Estimators (RIDE); PixelRNN and PixelCNN; Plug-and-Play generative networks.6. Generative Adversarial Network (GAN):GAN; Deep Convolutional GAN; Conditional GAN; Applications.7. Deep Learning for Image Processing and Computational ImagingDenoising; Deblurring; Super-resolution; Color Filter Array design.
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