Konda Reddy Mopuri

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indian institute of science

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∙ 06/17/2021

Class Balancing GAN with a Classifier in the Loop

Generative Adversarial Networks (GANs) have swiftly evolved to imitate i...

Harsh Rangwani, et al. ∙

∙ 01/15/2021

Data Impressions: Mining Deep Models to Extract Samples for Data-free Applications

Pretrained deep models hold their learnt knowledge in the form of the mo...

Gaurav Kumar Nayak, et al. ∙

∙ 11/18/2020

Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation

Knowledge Distillation is an effective method to transfer the learning a...

Gaurav Kumar Nayak, et al. ∙

∙ 06/10/2020

Dataset Condensation with Gradient Matching

Efficient training of deep neural networks is an increasingly important ...

Bo Zhao, et al. ∙

∙ 04/27/2020

Adversarial Fooling Beyond "Flipping the Label"

Recent advancements in CNNs have shown remarkable achievements in variou...

Konda Reddy Mopuri, et al. ∙

∙ 01/08/2020

iDLG: Improved Deep Leakage from Gradients

It is widely believed that sharing gradients will not leak private train...

Bo Zhao, et al. ∙

∙ 05/20/2019

Zero-Shot Knowledge Distillation in Deep Networks

Knowledge distillation deals with the problem of training a smaller mode...

Gaurav Kumar Nayak, et al. ∙

∙ 08/06/2018

Gray-box Adversarial Training

Adversarial samples are perturbed inputs crafted to mislead the machine ...

Vivek B. S., et al. ∙

∙ 08/03/2018

Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions

Deep learning models are susceptible to input specific noise, called adv...

Konda Reddy Mopuri, et al. ∙

∙ 01/24/2018

Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations

Machine learning models are susceptible to adversarial perturbations: sm...

Konda Reddy Mopuri, et al. ∙

∙ 12/09/2017

NAG: Network for Adversary Generation

Adversarial perturbations can pose a serious threat for deploying machin...

Konda Reddy Mopuri, et al. ∙

∙ 08/22/2017

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

Deep convolutional neural networks (CNN) have revolutionized various fie...

Konda Reddy Mopuri, et al. ∙

∙ 07/18/2017

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

State-of-the-art object recognition Convolutional Neural Networks (CNNs)...

Konda Reddy Mopuri, et al. ∙

∙ 05/25/2017

Deep image representations using caption generators

Deep learning exploits large volumes of labeled data to learn powerful m...

Konda Reddy Mopuri, et al. ∙

∙ 01/25/2016

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the d...

Suraj Srinivas, et al. ∙

∙ 04/24/2015

Object Level Deep Feature Pooling for Compact Image Representation

Convolutional Neural Network (CNN) features have been successfully emplo...

Konda Reddy Mopuri, et al. ∙