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Explore how deep neural networks can learn new tasks without catastrophically forgetting previous knowledge through regularization, dynamic architectures, and generative replay.
Explore how variational autoencoders generate data through latent codes, balancing reconstruction accuracy and distribution regularization with the variational lower bound.
Analyze how learning rates shape the convergence, divergence, and optimization behavior of gradient descent and stochastic gradient descent in deep neural networks.
Explores how small, human-imperceptible perturbations fool deep neural networks, covering white- and black-box attacks, physical-world examples, and adversarial training defenses.
Survey neural network architectures for image classification, segmentation, restoration, super-resolution, and reconstruction, including convolutional, residual, encoder-decoder, and autoencoder designs.
Review supervised learning through loss functions, bias-variance tradeoffs, and the surprising double-descent behavior of highly overparameterized neural networks.
Explore unlearned neural networks as image priors for solving imaging inverse problems without training data.
A theoretical lecture on using GAN priors to recover natural images from underdetermined compressed-sensing measurements.
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