Generative models for fixing image defects
Who is this presentation for?
- Machine learning engineers
When you take any photograph using a digital camera, defects can be introduced depending on lighting, exposure, etc., which can include underexposure or overexposure, out-of-focus blur, etc. You can end up spending a lot of time on image-editing software fixing these defects.
Akhilesh Kumar investigates approaches that make it easy to identify the region of the defects. You’ll see a general adversarial network- (GAN) based solution that can automatically fix some defects, like over- or underexposure. Akhilesh details how a GAN-based solution can be better than the traditional approach of correcting images, and how this can be applied on defective images by treating videos frame by frame.
- An understanding of machine learning and deep learning
What you'll learn
- Find out how cutting-edge technology like GAN can be used for fixing image defects
- Learn about GANs
- Understand how deep learning-based methods can be better than traditional algorithms
Akhilesh Kumar is a senior machine learning engineer on the applied machine learning team at Adobe, where he’s primarily responsible for putting deep learning models in production. Part of his job is to train, evaluate, and put deep learning models in scalable systems. He’s an avid reader and loves to come up with solutions for a wide variety of problems.
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