Lecture

Generative Adversarial Networks: Data Synthesis Techniques

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Description

This lecture covers the principles and applications of Generative Adversarial Networks (GANs) in deep learning. It begins with a review of previous topics, including manipulation of objects and embodied models. The instructor introduces GANs, explaining their role in synthesizing data from noise and translating images across domains. Key concepts such as the generator and discriminator roles in GANs are discussed, along with the challenges of balancing their performance. The lecture delves into advanced GAN types, including Conditional GANs, Auxiliary Classifier GANs, and InfoGANs, highlighting their unique features and applications. The Wasserstein distance is introduced as a metric for evaluating GAN performance, emphasizing its advantages in training stability. The session also includes practical examples of GAN applications, such as Pix2Pix and StyleGAN, showcasing their capabilities in generating high-quality images. The lecture concludes with a discussion on the desirable properties of synthetic data and the importance of evaluation metrics in assessing GAN effectiveness.

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

Related concepts (32)

Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning framework and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Computer vision

Computer vision tasks include methods for , , and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input to the retina in the human analog) into descriptions of the world that make sense to thought processes and can elicit appropriate action.

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