Research
I'm interested in computer vision, machine learning, optimization, graphical models.
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Role of Spatial Context in Adversarial Robustness for Object Detection
Aniruddha Saha*, Akshayvarun Subramanya*, Koninika Patil, Hamed Pirsiavash *equal contribution
CVPR2020 Adversarial Machine learning Workshop, 2020
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Most fast object detection algorithms rely on using spatial context which we show can lead to decreased adversarial robustness. We propose regularization techniques to improve robustness and also discuss using interpretation algorithms in object detection networks.
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Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, Hamed Pirsiavash
Oral presentation at 34th American Conference on Artificial Intelligence(AAAI), 2020
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We explore poisoning methods to introduce backdoors in neural networks. The trigger for the backdoor is revealed only during inference and hidden during the model training stage, which gives more capacity to the adversary.
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Fooling Network Interpretation in Image Classification
Akshayvarun Subramanya*, Vipin Pillai*, Hamed Pirsiavash *equal contribution
International Conference on Computer vision (ICCV), 2019
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We show that popular network interpretation algorithms do not necessarily show the correct reasoning for network’s prediction, by using adversarial examples. Our work highlights the need for developing more robust interpretation tools to analyze a neural network’s prediction.
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BatchOut: Batch-level feature augmentation to improve robustness to adversarial examples
Akshayvarun Subramanya, Konda Reddy Mopuri, R Venkatesh Babu
11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2018
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We propose a novel feature augmentation technique which can lead to improved robustness against multiple adversarial methods.
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Confidence estimation in deep neural networks via density modelling
Akshayvarun Subramanya, Suraj Srinivas, R Venkatesh Babu
International Conference on Signal Processing and Communications (SPCOM), 2018
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We show that traditional softmax based confidence measures has drawbacks and propose a new confidence measure based on density modelling approaches. The proposed measure shows improvement for different kinds of noise introduced in images.
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Training Sparse Neural Networks
Suraj Srinivas, Akshayvarun Subramanya, R Venkatesh Babu
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Embedded vision workshop, 2017
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We propose a new framework of training neural networks which implicitly use sparse computations. We introduce additional gate parameters which help in pruning, resulting in state-of-the-art compression results for neural networks.
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