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Konda Reddy Mopuri

Indian Institute of Technology Guwahati

24 papers hereh-index 183.1k citations41 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author8
  • middle author13
  • last author2

Across the 23 of 24 papers where every author was matched, so the position is known.

fields
  • cs.CV15
  • cs.LG8
  • eess.IV1
affiliations
  • Indian Institute of Technology Guwahati
Homepage
same name
  • Konda Reddy Mopuri — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152026
most citediDLG: Improved Deep Leakage from Gradients

377 citations · 582 across the 13 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017

NAG: Network for Adversary Generation

Konda Reddy Mopuri, Utkarsh Ojha, Utsav Garg +1

Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifi…

cs.CV2017

CNN Fixations: An unraveling approach to visualize the discriminative image regions

Konda Reddy Mopuri, Utsav Garg, R. Venkatesh Babu

Deep convolutional neural networks (CNN) have revolutionized various fields of vision research and have seen unprecedented adoption for multiple tasks such as classification, detec…

cs.CV2017★ 105 cited

Fast Feature Fool: A data independent approach to universal adversarial perturbations

Konda Reddy Mopuri, Utsav Garg, R. Venkatesh Babu

State-of-the-art object recognition Convolutional Neural Networks (CNNs) are shown to be fooled by image agnostic perturbations, called universal adversarial perturbations. It is a…

cs.CV2017

Deep image representations using caption generators

Konda Reddy Mopuri, Vishal B. Athreya, R. Venkatesh Babu

Deep learning exploits large volumes of labeled data to learn powerful models. When the target dataset is small, it is a common practice to perform transfer learning using pre-trai…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.