Stanford CS230: Deep Learning | Autumn 2018 | Lecture 4 - Adversarial Attacks / GANs

TL;DR

Generative Adversarial Networks (GANs) are used to train a generator to create images that resemble real-world examples, while a discriminator distinguishes between real and fake images.

Transcript

Okay. Let's get started, guys. So welcome to lecture number 4. Um, today we will go over two topics that are not discussed, uh, in the Coursera videos. Uh, you've been learning C2M1 and C2M2, if I'm not mistaking. So you've learned about, uh, what, uh, an initialization is, how to tune neural networks, what tests validation and train sets are. Toda... Read More

Key Insights

• 🇺🇬 GANs consist of a generator and discriminator, with the generator learning to generate realistic images and the discriminator learning to distinguish between real and fake images.
• 🇺🇬 GANs can be used for tasks such as image-to-image translation, where the generator learns to convert images from one domain to another.
• 🚂 GANs can be challenging to train, with various issues such as mode collapse and instability during training.

Questions & Answers

Q: What is the purpose of the discriminator in a GAN?

The discriminator is trained to distinguish between real and fake images, providing feedback to the generator to improve its image generation.

Q: How do GANs train the generator to create realistic images?

GANs use a feedback loop between the generator and discriminator, with the discriminator guiding the generator to generate more realistic images over time.

Q: How can GANs be used for image-to-image translation?

GANs can learn to convert images from one domain to another, such as converting horse images to zebra images, by training the generator to learn the mapping between the two domains.

Q: What are some challenges in training GANs?

GANs can be difficult to train, and common challenges include mode collapse, vanishing/exploding gradients, and instability during training.

Summary & Key Takeaways

• GANs consist of a generator and a discriminator, with the generator learning to generate realistic images and the discriminator learning to distinguish between real and fake images.

• The training process involves a feedback loop where the discriminator provides feedback to the generator, helping it improve its image generation.

• GANs can be used for tasks such as image-to-image translation, where the generator learns to convert images from one domain to another.