Search Results for author: Konda Reddy Mopuri

Found 17 papers, 9 papers with code

Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation

no code implementations CVPR 2023 Gaurav Patel, Konda Reddy Mopuri, Qiang Qiu

To this end, at every generator update, we aim to maintain the student's performance on previously encountered examples while acquiring knowledge from samples of the current distribution.

Data-free Knowledge Distillation Meta-Learning +1

Class Balancing GAN with a Classifier in the Loop

1 code implementation17 Jun 2021 Harsh Rangwani, Konda Reddy Mopuri, R. Venkatesh Babu

However, majority of the developments focus on performance of GANs on balanced datasets.

Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation

no code implementations18 Nov 2020 Gaurav Kumar Nayak, Konda Reddy Mopuri, Anirban Chakraborty

In such scenarios, existing approaches either iteratively compose a synthetic set representative of the original training dataset, one sample at a time or learn a generative model to compose such a transfer set.

Data-free Knowledge Distillation Transfer Learning

Dataset Condensation with Gradient Matching

4 code implementations ICLR 2021 Bo Zhao, Konda Reddy Mopuri, Hakan Bilen

As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive.

Continual Learning Dataset Condensation +2

Adversarial Fooling Beyond "Flipping the Label"

no code implementations27 Apr 2020 Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu

Therefore, the metric to quantify the vulnerability of the models should capture the severity of the flipping as well.

iDLG: Improved Deep Leakage from Gradients

2 code implementations8 Jan 2020 Bo Zhao, Konda Reddy Mopuri, Hakan Bilen

Particularly, our approach can certainly extract the ground-truth labels as opposed to DLG, hence we name it Improved DLG (iDLG).

Federated Learning valid

Zero-Shot Knowledge Distillation in Deep Networks

1 code implementation20 May 2019 Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu, Anirban Chakraborty

Without even using any meta-data, we synthesize the Data Impressions from the complex Teacher model and utilize these as surrogates for the original training data samples to transfer its learning to Student via knowledge distillation.

Knowledge Distillation

Gray-box Adversarial Training

no code implementations ECCV 2018 Vivek B. S., Konda Reddy Mopuri, R. Venkatesh Babu

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

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

no code implementations ECCV 2018 Konda Reddy Mopuri, Phani Krishna Uppala, R. Venkatesh Babu

Given a model, there exist broadly two approaches to craft UAPs: (i) data-driven: that require data, and (ii) data-free: that do not require data samples.

Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations

2 code implementations24 Jan 2018 Konda Reddy Mopuri, Aditya Ganeshan, R. Venkatesh Babu

Further, via exploiting simple priors related to the data distribution, our objective remarkably boosts the fooling ability of the crafted perturbations.

Adversarial Attack Depth Estimation +2

NAG: Network for Adversary Generation

1 code implementation CVPR 2018 Konda Reddy Mopuri, Utkarsh Ojha, Utsav Garg, R. Venkatesh Babu

Our trained generator network attempts to capture the distribution of adversarial perturbations for a given classifier and readily generates a wide variety of such perturbations.

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

2 code implementations22 Aug 2017 Konda Reddy Mopuri, Utsav Garg, R. Venkatesh Babu

We demonstrate through a variety of applications that our approach is able to localize the discriminative image locations across different network architectures, diverse vision tasks and data modalities.

Image Captioning Object Recognition

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

1 code implementation18 Jul 2017 Konda Reddy Mopuri, Utsav Garg, R. Venkatesh Babu

In this paper, for the first time, we propose a novel data independent approach to generate image agnostic perturbations for a range of CNNs trained for object recognition.

Object Recognition

Deep image representations using caption generators

1 code implementation25 May 2017 Konda Reddy Mopuri, Vishal B. Athreya, R. Venkatesh Babu

We demonstrate that, owing to richer supervision provided during the process of training, the features learned by the captioning system perform better than those of CNNs.

Retrieval Transfer Learning

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

no code implementations25 Jan 2016 Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri, Nikita Prabhu, Srinivas S. S. Kruthiventi, R. Venkatesh Babu

With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective.

Object Level Deep Feature Pooling for Compact Image Representation

no code implementations24 Apr 2015 Konda Reddy Mopuri, R. Venkatesh Babu

Convolutional Neural Network (CNN) features have been successfully employed in recent works as an image descriptor for various vision tasks.

Image Retrieval Object +2

Cannot find the paper you are looking for? You can Submit a new open access paper.