pattern recognization ppt.pptx
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The document discusses generative models in Bayesian theory. It provides an overview of generative modeling and generative adversarial networks (GANs). GANs use two neural networks, a generator and discriminator, that compete against each other. The generator generates fake samples to fool the discriminator, while the discriminator tries to distinguish real from fake samples. Applications of GANs include generating images, music, text-to-image generation and more. Bayesian probability theory provides a framework for reasoning about hypotheses given data using the likelihood, prior, and posterior. Generative models aim to represent observed phenomena using probability distributions learned from data.
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- 1. University Institute of Science Master of Science (Data Science) Presented by: REETU UID: 21MSM3053 TOPIC: Generative models in Bayesian theory Submitted to: Er. Gagninder Kaur
- 2. Topics to be covered Generative Modelling What are GANs Applications of GAN Difference between generator and discriminator Generative models in Bayesian theory
- 3. Introduction Generative modelling Generative modelling is unsupervised machine learning approach. Generative modeling is the use of artificial intelligence, statistics and probability in applications to produce a representation or abstraction of observed phenomena or target variables that can be calculated from observations.
- 4. What are GANs? GANs stands for Generative adversarial network. GANs are just one kind of generative models. In GANs, there is a generator and a discriminator. The Generator generates fake samples of data (be it an image, audio, etc.) and tries to fool the Discriminator. The Discriminator, on the other hand, tries to distinguish between the real and fake samples. The Generator and the Discriminator are both Neural Networks and they both run in competition with each other in the training phase. The steps are repeated several times and in this, the Generator and Discriminator get better and better in their respective jobs after each repetition.
- 5. Applications of GANs • It generates new data from the available data ,mostly creating and producing images but not limited to images only. • Generate music by using some clone voice. • Text to image generation. • Creation of anime characters in game development and animation production. • Image to image translation • Low resolution to high resolution.
- 6. Difference between generator and discriminator Generator and discriminator are two components of GAN. Generator is unsupervised approach. Discriminator is supervised approach . X G Fake Data x’ D T/ F Backpropagation Generator Real data Discriminator
- 7. Generative models in Bayesian theory Bayesian probability theory provides a mathematical framework for performing inference, or reasoning, using probability. In Bayesian probability theory, one of these “events” is the hypothesis, H, and the other is data, D, and we wish to judge the relative truth of the hypothesis given the data. According to Bayes’ rule, we do this via the relation The term P(D|H) is called the likelihood function and it assesses the probability of the observed data arising from the hypothesis. The term P(H) is called the prior, as it reflects one’s prior knowledge before the data are considered. Finally, the term P(H|D) is known as the posterior, and as its name suggests, reflects the probability of the hypothesis after consideration of the data.
- 8. References • https://redwood.berkeley.edu/wp- content/uploads/2018/08/Bayes-prob-generative.pdf • https://stats.stackexchange.com/questions/7455/the-connection- between-bayesian-statistics-and-generative-modeling • https://www.cs.toronto.edu/~guerzhoy/411/lec/W07/generative/ • https://lingpipe-blog.com/2013/05/23/all-bayesian-models-are- generative-in-theory/