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.
1 code implementation • 17 Jun 2021 • Harsh Rangwani, Konda Reddy Mopuri, R. Venkatesh Babu
However, majority of the developments focus on performance of GANs on balanced datasets.
no code implementations • 15 Jan 2021 • Gaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain, Anirban Chakraborty
We dub them "Data Impressions", which act as proxy to the training data and can be used to realize a variety of tasks.
no code implementations • 18 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.
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.
no code implementations • 27 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.
2 code implementations • 8 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).
1 code implementation • 20 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.
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.
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.
2 code implementations • 24 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.
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.
2 code implementations • 22 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.
1 code implementation • 18 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.
1 code implementation • 25 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.
no code implementations • 25 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.
no code implementations • 24 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.