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Creators/Authors contains: "Mohan, Deen Dayal"

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  1. ; ; ; ; ; (, Advances in Neural Information Processing Systems 37 (NeurIPS 2024))
    Free, publicly-accessible full text available December 1, 2025
  2. ; ; ; ; (, IEEE)
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  3. ; ; ; ; ; ; (, KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
    In this paper we present AMPNet, an acoustic abnormality detection model deployed at ACV Auctions to automatically identify engine faults of vehicles listed on the ACV Auctions platform. We investigate the problem of engine fault detection and discuss our approach of deep-learning based audio classification on a large-scale automobile dataset collected at ACV Auctions. Specifically, we discuss our data collection pipeline and its challenges, dataset preprocessing and training procedures, and deployment of our trained models into a production setting. We perform empirical evaluations of AMPNet and demonstrate that our framework is able to successfully capture various engine anomalies agnostic of vehicle type. Finally we demonstrate the effectiveness and impact of AMPNet in the real world, specifically showing a 20.85% reduction in vehicle arbitrations on ACV Auctions' live auction platform. 
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  4. ; ; ; ; ; (, CoNEXT 2020)
    null (Ed.)
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  5. ; ; ; ; (, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW))
    The security of biometric templates is of paramount importance. Leakage of biometric information may result in loss of private data and can lead to the compromise of the biometric system. Yet, the security of templates is often overlooked in favour of performance. In this paper, we present a plug-and-play framework for creating secure face templates with negligible degradation in the performance of the system. We propose a significant bit based representation which guarantees security in addition to other biometric aspects such as cancelability and reproducibility. In addition to being scalable, the proposed method does not make unrealistic assumptions regarding the pose or illumination of the face images. We provide experimental results on two unconstrained datasets - IJB-A and IJB-C. 
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