Course Details

Course details of EE6132W
Course NoEE6132W
Course TitleAdvanced Topics in Signal Processing
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. Autoencoders: Autoencoder; Denoising auto-encoder; Sparse auto-encoder; Variational autoencoder; Applications in imaging such as segnet and image generation. 3. 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. 4. Recurrent Neural Network (RNN): Basic RNN; Long Short Term Memory (LSTM); Applications in imaging such as activity recognition. 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 Imaging Denoising; Deblurring; Super-resolution; Color Filter Array design.
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