GANs Presentation.pptx
- 1. Generative Adversarial Networks (GANs) Students: Mahmoud El-halaq Ammar El-laham Supervisor: Eng. Akram Abu Garad
- 2. Abstract ● Generative Adversarial Networks (GANs) are a type of deep learning algorithm that can generate new data that is similar to a given training dataset. ● GANs consist of two neural networks: a generator network that produces new data samples, and a discriminator network that tries to distinguish between the generated samples and the real ones. The two networks are trained together in an adversarial setting, where the generator aims to produce samples that are realistic enough to fool the discriminator, while the discriminator tries to accurately identify which samples are real and which ones are generated.
- 3. Outline ● Introduction ● Machine Learning ○ Supervised Learning ○ Unsupervised Learning ● What are GANs? ○ Generative model ○ Discriminative model ● How do GANs work? ● Examples of GANs ● GANs Applications ● Advantages & Disadvantages ● Conclusion
- 4. Introduction ● Generative Adversarial Networks, or GANs, are a type of neural network architecture that have gained a lot of attention in recent years due to their ability to generate new, high- quality, realistic data. GANs were first introduced in 2014 by Ian Goodfellow and his colleagues. ● The basic idea behind GANs is that they consist of two neural networks: a generator and a discriminator. The generator network takes random noise as input and produces a new piece of data, such as an image or a sound clip. The discriminator network takes this new piece of data, along with real examples of the same type of data, and tries to distinguish between them. The two networks are trained simultaneously, with the generator trying to produce data that is indistinguishable from the real data, and the discriminator trying to correctly identify which data is real and which is generated. ● GANs have a wide range of applications, including generating realistic images, videos, and music, as well as in natural language processing tasks such as text generation and machine translation.
- 5. Machine Learning Machine learning is a type of artificial intelligence (AI) that allows computers to learn and improve from experience without being explicitly programmed. In other words, instead of being told what to do, machines can learn from data and make predictions or decisions based on that data. Types of machine learning: ● Supervised Learning ● Unsupervised Learning ● Reinforcement Learning
- 6. Machine Learning ● Supervised learning is a type of machine learning that uses labeled data to train machine learning models. In labeled data, the output is already known. The model just needs to map the inputs to the respective outputs. ● This correction of the model is generally referred to as a supervised form of learning, or supervised learning. ● Examples: Linear Regression, Logistic Regression, Decision Tree. Example of Supervised Learning
- 7. Machine Learning ● Unsupervised learning is a type of machine learning that uses unlabeled data to train machines. Unlabeled data doesn’t have a fixed output variable. The model learns from the data, discovers the patterns and features in the data, and returns the output. ● Examples: problems include clustering, generative modeling, learning algorithms are K-means and Generative Adversarial Networks. Example of Unsupervised Learning
- 8. What are GANs? ● Generative adversarial networks are implicit likelihood models that generate data samples from the statistical distribution of the data. They’re used to copy variations within the dataset. They use a combination of two networks: generator and discriminator.
- 9. What are GANs? ● The generator generates a batch of samples, and these, along with real examples from the domain, are provided to the discriminator and classified as real or fake. ● The discriminator is then updated to get better at discriminating real and fake samples in the next round, and importantly, the generator is updated based on how well, or not, the generated samples fooled the discriminator.
- 10. What are GANs? ● They both play an adversarial game where the generator tries to fool the discriminator by generating data similar to those in the training set. The Discriminator tries not to be fooled by identifying fake data from real data.
- 11. How do GAN’s work?
- 12. How do GAN’s work?
- 13. How do GAN’s work?
- 14. How do GAN’s work?
- 15. Examples of GANs
- 16. Examples of GANs
- 17. GANs Applications ● With the help of DCGANs, you can train images of cartoon characters for generating faces of anime characters as well as Pokemon characters.
- 18. GANs Applications ● GANs can be trained on the images of humans to generate realistic faces. The faces that you see below have been generated using GANs and do not exist in reality.
- 19. Advantages ● GANs can generate high-quality, realistic synthetic data that closely resembles real-world data. ● GANs have a wide range of applications, including image and video synthesis, text-to-image generation, and music synthesis, among others. ● GANs can be used to generate data with specific characteristics, such as generating images of a particular style or genre. ● GANs can be used for data augmentation, which can help improve the performance of machine learning models.
- 20. Disadvantages ● GANs can be challenging to train, and the training process can be unstable and time-consuming. ● GANs can suffer from mode collapse, where the generator produces a limited set of outputs that do not represent the full range of possible outputs. ● GANs can produce biased outputs if the training data is biased, which can perpetuate and amplify existing biases in the data. ● GANs can be computationally expensive, particularly for large and complex datasets.
- 21. Conclusion GANs have a wide range of applications, including image and video synthesis, text-to-image generation, and music synthesis, among others. However, GANs also face several challenges, including instability during training, mode collapse, and difficulty in controlling the output. Therefore, researchers are continually developing new variants of GANs and exploring techniques to address these challenges.
- 22. Thank You