Internship: Generative Adversarial Networks (GANs) for Learning and Inference
GANs (generative adversarial networks) and related methods (such as variational autoencoders) have generated much excitement for their ability to synthesize images and data that appear remarkably realistic. MERL is seeking a highly motivated intern to conduct original research in the area of generative adversarial networks with the goal of improving performance on real-world tasks. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The ideal candidate would be a senior PhD student in computer vision with experience in GANs and related deep learning methods, as well as strong general knowledge in machine learning. Strong programming skills and previous experience coding GANs are expected. Duration: 3 months.
Research Area: Computer Vision
Contact: Tim Marks