377 citations · 860 across the 18 of their papers we have counts for
16 papers · 1 filter
Parcel2Progression: An Anatomy-aware Longitudinal Framework for Alzheimer's Disease Diagnosis
Madhumitha Venkatesh, Shanawaj S Madarkar, Konda Reddy Mopuri
Alzheimer's disease (AD) progression is a longitudinal process with subtle pathological cues in the early stages. Yet, computational constraints have limited most neuroimaging mode…
Exploring Compositionality in Vision Transformers using Wavelet Representations
Akshad Shyam Purushottamdas, Pranav K Nayak, Divya Mehul Rajparia +4
While insights into the workings of the transformer model have largely emerged by analysing their behaviour on language tasks, this work investigates the representations learnt by…
DiRe: Diversity-promoting Regularization for Dataset Condensation
Saumyaranjan Mohanty, Aravind Reddy, Konda Reddy Mopuri
In Dataset Condensation, the goal is to synthesize a small dataset that replicates the training utility of a large original dataset. Existing condensation methods synthesize datase…
Lost in Context: The Influence of Context on Feature Attribution Methods for Object Recognition
Sayanta Adhikari, Rishav Kumar, Konda Reddy Mopuri +1
Contextual information plays a critical role in object recognition models within computer vision, where changes in context can significantly affect accuracy, underscoring models' d…
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data
Gaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain +1
Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen…
Dataset Condensation with Gradient Matching
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. This pape…
Adversarial Fooling Beyond "Flipping the Label"
Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu
Recent advancements in CNNs have shown remarkable achievements in various CV/AI applications. Though CNNs show near human or better than human performance in many critical tasks, t…
Gray-box Adversarial Training
Vivek B. S., Konda Reddy Mopuri, R. Venkatesh Babu
Adversarial samples are perturbed inputs crafted to mislead the machine learning systems. A training mechanism, called adversarial training, which presents adversarial samples alon…
Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions
Konda Reddy Mopuri, Phani Krishna Uppala, R. Venkatesh Babu
Deep learning models are susceptible to input specific noise, called adversarial perturbations. Moreover, there exist input-agnostic noise, called Universal Adversarial Perturbatio…
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations
Konda Reddy Mopuri, Aditya Ganeshan, R. Venkatesh Babu
Machine learning models are susceptible to adversarial perturbations: small changes to input that can cause large changes in output. It is also demonstrated that there exist input-…
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…
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…
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…
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…
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri +3
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 lea…
Object Level Deep Feature Pooling for Compact Image Representation
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. But the inability of the deep CNN featu…