Arun Mallya

I am a Research Scientist at Nvidia Research.
I graduated with a PhD from the University of Illinois at Urbana-Champaign, where I explored computer vision under the guidance of Prof. Svetlana Lazebnik.
I previously completed my Master's in CS at UIUC, and my Bachelor's in CSE at IIT KGP.

My research focus is on doing more with neural networks.

email / Github / LinkedIn / Scholar / webpage



A M
Research
Generative Networks

New!
Generative Adversarial Networks for Image and Video Synthesis:
Algorithms and Applications

Ming-Yu Liu*, Xun Huang*, Jiahui Yu*, Ting-Chun Wang*, Arun Mallya*
[arxiv preprint]

New!
World-Consistent Video-to-Video Synthesis
Arun Mallya*, Ting-Chun Wang*, Karan Sapra, Ming-Yu Liu
European Conference on Computer Vision (ECCV), 2019
[arxiv preprint] [project webpage and code]

Few-Shot Unsupervised Image-to-Image Translation
Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, Jan Kautz
International Conference on Computer Vision (ICCV), 2019
[arxiv preprint] [project webpage and code]

Efficient and Multi-Task Networks

New!
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
Hongxu Yin, Pavlo Molchanov, Zhizhong Li, Jose Alvarez, Arun Mallya, Derek Hoiem, Niraj Jha, Jan Kautz
Oral Presentation
Computer Vision and Pattern Recognition (CVPR), 2020
[arxiv preprint] [code]

New!
UNAS: Differentiable Architecture Search Meets Reinforcement Learning
Arash Vahdat, Arun Mallya, Ming-Yu Liu, Jan Kautz
Oral Presentation
Computer Vision and Pattern Recognition (CVPR), 2020
[arxiv preprint]

Importance Estimation for Neural Network Pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, Jan Kautz
Computer Vision and Pattern Recognition (CVPR), 2019
[arxiv preprint] [code]

Piggyback: Adding Multiple Tasks to a Single, Fixed Network by Learning to Mask
Arun Mallya, Dillon Davis, Svetlana Lazebnik
European Conference on Computer Vision (ECCV), 2018
[arxiv preprint] [code]

PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
Arun Mallya, Svetlana Lazebnik
Computer Vision and Pattern Recognition (CVPR), 2018
[arxiv preprint] [code]

Vision and Language

Recurrent Models for Situation Recognition
Arun Mallya, Svetlana Lazebnik
International Conference on Computer Vision (ICCV), 2017
[arxiv preprint] [poster]

Phrase Localization and Visual Relationship Detection with Comprehensive Linguistic Cues
Bryan A. Plummer, Arun Mallya, Christopher M. Cervantes, Julia Hockenmaier, Svetlana Lazebnik
International Conference on Computer Vision (ICCV), 2017
[arxiv preprint]

Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering
Arun Mallya, Svetlana Lazebnik
European Conference on Computer Vision (ECCV), 2016
[arxiv preprint] [caffemodels]

Solving Visual Madlibs with Multiple Cues
Tatiana Tommasi, Arun Mallya, Bryan Plummer, Svetlana Lazebnik, Alex Berg, Tamara Berg
British Machine Vision Conference (BMVC), 2016
[arxiv preprint]
International Journal of Computer Vision (IJCV), 2016, submitted
[arxiv preprint]

Trained models: [caffemodels]

  1. The Fusion model from the ECCV'16 paper above, trained on the HICO action dataset to predict 600 human-object interactions.
  2. The Fusion model from the ECCV'16 paper above, trained on the MPII action dataset to predict 393 human actions.
  3. A VGG-16 model to predict 302 person attributes selected from the Flickr30k Entities dataset.

Structured Prediction

Learning Informative Edge Maps for Indoor Scene Layout Prediction
Arun Mallya, Svetlana Lazebnik
International Conference on Computer Vision (ICCV), 2015
[pdf] [hedau+ dataset] [hedau+ processed data]
[hedau+ LMDBs (14GB)] [trained model + demo]

Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization
Kevin J. Shih, Arun Mallya, Saurabh Singh, Derek Hoiem
British Machine Vision Conference (BMVC), 2015
[arxiv preprint] [poster] [webpage]

Unsupervised Deep Network Pretraining via Human Design
Ming-Yu Liu, Arun Mallya, Oncel C. Tuzel, Xi Chen
Winter Conference on Applications of Computer Vision (WACV), 2016
Work performed as a summer intern at MERL Cambridge
[arxiv preprint]


Tutorials
  1. Accelerating Computer Vision with Mixed Precision, ECCV 2020
  2. Accelerating Computer Vision with Mixed Precision, ICCV 2019

Writeups/Notes
Hosted on github. Edit requests/additions/corrections are welcome.
  1. A Backpropagation Refresher
  2. An Illustrated Explanation of the LSTM Forward-Backward Pass
  3. Introduction to RNNs
  4. Introduction to RNNs - II
  5. Jupyter notebook to find Receptive Field Size and Effective Stride (supports dilated convs)
  6. Visualization of neuron connections and receptive field of a CNN (including dilation)!

(imitation is the sincerest form of flattery)