Generative Adversarial Networks (GANs)

Deep learning has accomplished pronounced triumph in the field of artificial intelligence, there are many deep learning models that have been developed in the recent time. Generative Models (GAN) are one of the deep learning models, that was given based on the game theory called zero-sum and now has been treated as the hot area for research. Generative Models are modern techniques used in computer vision. Unlike other neural networks that are used for predictions from images, generative models can generate new images as well for specific objectives. They can be used for generating huge datasets. This session will review several applications of generative modeling such as image generation and image translation, video frame prediction using CNNs and GANs.

 
 

Outline/Structure of the Demonstration

• Introduction(2 mins)
• ABCs of GANs (1 mins)
• Supervised and Unsupervised Learning(2 mins)
• Generative And Discriminative Models.(2 mins)
• Magic of GANs(2 mins)
• Architecture(2 min)
o Discriminator
o Generator
• Formulation(2 min)
• Algorithm and insights(3 mins)
• Hands on Demo(5 mins)
• Questions

Learning Outcome

Basics of GAN, how some of the applications work, deep insight into GANs, working with combination of CNN and GAN based architecture

Target Audience

Data Scientists, Data Engineers, Data Specialists, Machine Learning Engineers, Artificial Intelligence Enthusiasts,Machine learning Researchers, Artificial intelligence researchers

Prerequisites for Attendees

Basic knowledge of Neural networks and an interest in modern trends in Artificial Intelligence

schedule Submitted 3 months ago

Public Feedback

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  • Deepti Tomar
    By Deepti Tomar  ~  3 months ago
    reply Reply

    Hello Saakshi & Vrishank,

    Thanks for your time and efforts on the proposal! Could you answer the following questions to help the program committee understand your proposal better?

    • Are you going to share demo(s) /use case(s) from your project work (industry-specific use cases)? Speaker's experience on the project helps people understand the concept better.
    • Did you use these techniques to help solve a particular problem? 
    • If yes, would you be sharing the Challenges faced in the implementation of the technique in your application and the workarounds?

    Thanks,

    Deepti

    • Vrishank Gupta
      By Vrishank Gupta  ~  3 months ago
      reply Reply

      Hello Deepti,

      Thanks for your interest in our proposal. 

      1. Yes, we are going to share the live demo and different use cases from our project work by generating new images using our code. We might demonstrate a few other applications as well if time permits. We would definitely love to share our experience of building the project to the attendees.

      2. Yes, we used GANs for image generation and would definitely demonstrate that during the conference.

      3. Yes, we would love to share the challenges that we faced during the implementation of our techniques and the solutions that we came up with. We would also love to hear feedback and alternative better solutions that attendees might address for our challenges. We always love to hear from experienced people of the industry and we think this is an amazing platform to learn and share experiences.

       

      Regards,

      Vrishank

       

      • Deepti Tomar
        By Deepti Tomar  ~  2 months ago
        reply Reply

        Hello Vrishank,

        Could you give more details on the real-world implementation of your project work? In which industry/domain this was applied?

        Thanks,

        Deepti

        • Vrishank Gupta
          By Vrishank Gupta  ~  2 months ago
          reply Reply

          Hello Deeti,

          We applied GANs for image data generation. We generated loads of images with corresponding labels that can be used for training various models.

      • Deepti Tomar
        By Deepti Tomar  ~  3 months ago
        reply Reply

        Thanks for your response, Vrishank! We will let you know in case if we have more questions.


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