5 Common GAN Interview Questions and How to Prepare

If you are preparing for an interview that will include the topic of generative adversarial networks (GANs), you might be interviewing for a role as a generative AI engineer, research scientist, or data scientist. In these roles, you can work on projects that develop new GAN models, find ways to apply GAN networks in new ways or generate images, text, data, and more using GAN models. 

To start working in GAN technology, you’ll first need to successfully complete an interview. Explore these five common GAN interview questions you might encounter to help you prepare for success. 

5 common GAN interview questions

To prepare for an interview as a generative AI engineer or AI research scientist, you may need to answer questions about GAN models. If the role you’re applying for works heavily with GAN models, this topic could be important for your interview. Many AI positions work with different types of technology, so GAN interview questions could be one of many types of AI tech questions you should be prepared to answer. Explore these five common questions you might be asked at an interview for a job that requires GAN skills. 

What are generative adversarial networks (GANs)?

What they’re really asking: This basic question is an easy way to open an interview, as your interviewer expects it to be easy for you to answer. They will be looking to see that you are comfortable with the topic, which they will notice by the ease with which you can offer a detailed description of how this technology works. 

The answer: A GAN is a neural network model made of two separate neural networks: the discriminator and the generator. Using adversarial training, the two networks work to outsmart each other. The generator learns to create a fake image that looks indistinguishable from real data. Meanwhile, the discriminator learns to spot these fake images. They use machine learning to learn from one another’s mistakes until, finally, the generator can produce an image so convincing that the discriminator can’t tell it from the training data. 

Other forms this question might take: 

• Explain the architecture of a basic GAN. 

• How does a GAN work? 

• How does a GAN generate content? 

• Describe the roles of the generator and discriminator in a GAN. 

What is the difference between types of GANs like cGAN and DCGAN?

What they’re really asking: This question gives your interviewer insight into your qualifications and potentially some of the experience you’ve had using advanced GAN architectures. They may also want to know if you have experience using the specific advanced architectures you’ll need in your job role. 

The answer: The difference between GAN models comes down to how the discriminator and generator relate to each other and which hidden layers the neural network model includes. A cGAN, or conditional generative adversarial network, includes labels that you add to have more control over what output the model generates. A DCGAN, or a deep convolutional generative adversarial network, includes hidden layers called convolutional and convolutional-transpose layers that make the neural network model better equipped to understand images and visual data. 

Other forms this question might take: 

• What types of generative models have you worked with?

• Describe a conditional GAN. 

• Explain the architecture of a DCGAN. 

• What is the difference between the architectures of a cGAN and a DCGAN?

How would you approach using GAN for text generation?

What they’re really asking: Your interviewer will want to understand more about how you’ve used GANs in the past. They will likely be asking this question about the primary way you will use GANs in your job role. This gives your interviewer insight into how well you’re qualified to manage the tasks in the position you’re applying for. It also gives your interviewer insight into how you approach problem-solving. 

The answer: You should personalize this question to your own professional experience, but you can mention some details about using GANs for text generation that demonstrate your expertise. You can mention the challenges of generating text with GAN models because they are better suited to creating images as opposed to text. You can also mention the different methods you’ve used in the past to overcome these challenges, such as using modified training objects, reinforcement learning, or Gumbel-Softmax differentiation. 

Other forms this question might take: 

• How have you applied GANs in previous roles? 

• Describe how you would approach training a GAN to generate text.

• What are the challenges with generating text using a GAN? 

• How can GANs be used for image generation, style transfer, and data augmentation?

What are some challenges in training GANs?

What they’re really asking: Similar to asking about using GANs for text generation, this question gives your interviewer additional insight into your experience and qualifications using GANs, as well as how you approach problem-solving. 

The answer: Two common challenges of training GANs are mode collapse and non-convergence. Mode collapse happens when the generator learns to create only one or two types of outputs instead of the variety you want it to produce. Two strategies to overcome mode collapse include using unrolled GANs or Wasserstein loss. Non-convergence is a problem when the discriminator model ceases to give useful feedback to the generator, which trains the generator on nonsense. You can overcome non-convergence by adding noise or adjusting the weights of the discriminator. 

Other forms this question might take: 

• What is mode collapse in GANs, and why is it problematic?

• How would you approach overcoming a failure to convergence with a GAN?

• What is the difference between mode collapse and non-convergence? 

How would you evaluate the performance and quality of GANs?

What they’re really asking: This question helps your interviewer understand your behavior and knowledge of the topic. Your answer should demonstrate that you understand the process, challenges, and benchmarks that measure success in a GAN. The question also asks about your behavior and how you would approach the problem, giving your interviewer insight into your reasoning and thought process. 

The answer: You should tailor your answer to the methods of evaluation you’ve used in the past or the methods that you feel are most effective. You could compare and contrast the two most common methods of evaluating GAN models: Inception score (IS) and Fréchet inception distance (FID) which use different mathematical calculations to offer a standard to measure generated images. You can also talk about concepts like a visual turing test, which submits an image through a series of binary questions that ultimately make a determination about whether the image is real or fake. 

Other forms this question might take: 

• How do you assess the quality of generated content?

• What methods would you use to determine the performance of a GAN?

• How can you measure the effectiveness of your GAN?

Preparing for a GAN interview questions on Coursera

To start working as a GAN engineer or AI research scientist, you will need to successfully navigate an interview where you can demonstrate your expertise on the topic. To prepare yourself with the knowledge and job skills you need, you can explore programs on Coursera to help you learn new skills, no matter what your career goals are. For example, IBM offers a series of Specializations in AI depending on the job role you want to fill, including the Generative AI for Data Scientists Specialization, the Generative AI for Data Analysts Specialization, and the Generative AI for Data Engineers Specialization. If you need a better fit your career goals, you can consider Generative AI Fundamentals Specialization to learn well-rounded skills you can use to apply generative AI to your work and personal life.