Note: Mehdi S. M. Sajjadi has transitioned from the institute (alumni). Explore further information here
This website is deprecated. Please visit msajjadi.com.
I am a PhD candidate in my final year under the supervision of Bernhard Schölkopf in the Empirical Inference department. Additionally, I am affiliated with Hendrik Lensch at the University of Tübingen and I am an ETH Zürich Center for Learning Systems associated PhD fellow. Previously, I had been working with Ulrike von Luxburg in the theory of machine learning group at the University of Hamburg.
My research interests include probabilistic and approximate algorithms, game AI, graph theory, computational photography, computer vision and machine learning along with its countless applications. During my PhD, I am focusing on creating efficient intelligent algorithms for use in image and video processing and perceptual metrics for evaluation. More generally, I am working on deep generative models.
Our work with convolutional generative adversarial neural networks has reached state-of-the-art results for the task of single image super-resolution in both quantitative and qualitative benchmarks. We have further reached state-of-the-art results in video super-resolution. A further line of work entails evaluating generative models such as GANs and improving their performance.
Please see the Projects tab for more information.
Machine Learning Computational Imaging Image Processing Neural Networks Deep Learning Generative Modeling Perceptual Evaluation Metrics
This website is deprecated. Please visit msajjadi.com.
> From Variational to Deterministic Autoencoders (ICLR 2020)
> Assessing Generative Models via Precision and Recall (NeurIPS 2018 and ICML 2018 workshop TADGM Oral and Best Poster Award at Bosch AI Con 2018)
> Photorealistic Video Super-Resolution with Enhanced Temporal Consistency (ECCV 2018 workshop PIRM)
> Spatio-temporal Transformer Network for Video Restoration (ECCV 2018)
> Tempered Adversarial Networks (ICML 2018 Oral and ICLR 2018 workshop)
> Frame-Recurrent Video Super-Resolution (CVPR 2018)
> EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (ICCV 2017 Oral)
- GitHub
- Our work has been covered in the press: