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Home Konda Reddy Mopuri. Konda Reddy Mopuri. Skip slideshow. Most frequent co-Author Konda Reddy Mopuri*[0000−0001−8894−7212], Phani Krishna Uppala*[0000−0003−0413−5685], and R. Venkatesh Babu[0000−0002−1926−1804] Video Analytics Lab, Indian Institute of Science, Bangalore, India Abstract. Deep learning models are susceptible to input specific noise, called adversarial perturbations.
thesis is titled ”Deep Visual Representations: A study on Augmentation, Visualization, and Robustness”. IUPRAI (Indian Unit of Pattern Recognition and Artificial Intelligence) – Doctoral Dissertation Award program has View Konda Reddy Mopuri’s profile on LinkedIn, the world’s largest professional community. Konda Reddy has 3 jobs listed on their profile. See the complete profile on LinkedIn and discover Konda Reddy’s connections and jobs at similar companies.
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I solve Computer Vision problems using Deep learning techniques. Also, I am MATLAB Central contributions by Konda Reddy Mopuri. Professional Interests: Image /Video Processing, Computer Vision, Data Science, Machine Learning. Authors: Konda Reddy Mopuri, Utsav Garg, R. Venkatesh Babu. Abstract: Deep convolutional neural networks (CNN) have revolutionized various fields of vision research and have seen unprecedented adoption for multiple tasks such as classification, detection, captioning, etc.
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[August 12,2015] Invited talk by Ravi Kiran Sarvadevabhatla and Mopuri Konda Reddy at IIIT-Bangalore [July 15,2015] Paper by R.Venkatesh Babu accepted in the Journal of Applied Soft Computing Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of Issue Date Title Author(s) 2018: CNN fixations : an unraveling approach to visualize the discriminative image regions: Mopuri, Konda Reddy; Garg, Utsav; Babu, R. Venkatesh View the profiles of professionals named "Konda Reddy" on LinkedIn.
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2017-07-18
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[August 12,2015] Invited talk by Ravi Kiran Sarvadevabhatla and Mopuri Konda Reddy at IIIT-Bangalore [July 15,2015] Paper by R.Venkatesh Babu accepted in the Journal of Applied Soft Computing Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of Issue Date Title Author(s) 2018: CNN fixations : an unraveling approach to visualize the discriminative image regions: Mopuri, Konda Reddy; Garg, Utsav; Babu, R. Venkatesh View the profiles of professionals named "Konda Reddy" on LinkedIn. There are 100+ professionals named "Konda Reddy", who use LinkedIn to exchange information, ideas, and opportunities.
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thesis is titled ”Deep Visual Representations: A study on Augmentation, Visualization, and Robustness”. IUPRAI (Indian Unit of Pattern Recognition and Artificial Intelligence) – Doctoral Dissertation Award program has View Konda Reddy Mopuri’s profile on LinkedIn, the world’s largest professional community. Konda Reddy has 3 jobs listed on their profile. See the complete profile on LinkedIn and discover Konda Reddy’s connections and jobs at similar companies. Konda Reddy Mopuri Author page based on publicly available paper data.
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Sort by citations Sort by year Sort by title. Cited by. Cited by. Year; A taxonomy of deep convolutional neural nets for computer vision. Konda Reddy Mopuri*[0000−0001−8894−7212], Phani Krishna Uppala*[0000−0003−0413−5685], and R. Venkatesh Babu[0000−0002−1926−1804] Video Analytics Lab, Indian Institute of Science, Bangalore, India Abstract. Deep learning models are susceptible to … Konda Reddy Mopuri, Phani Krishna Uppala, R. Venkatesh Babu; The European Conference on Computer Vision (ECCV), 2018, pp. 19-34 Abstract Deep learning models are susceptible to input specific noise, called adversarial perturbations.
Venkatesh Babu) from the Video Analytics Lab has won the IUPRAI Best Doctoral Dissertation Award for the year 2018-19. His Ph.D. thesis is titled ”Deep Visual Representations: A study on Augmentation, Visualization, and Robustness”.