62 citations · 245 across the 42 of their papers we have counts for
48 papers
Target-Aware Generative Augmentations for Single-Shot Adaptation
Kowshik Thopalli, Rakshith Subramanyam, Pavan Turaga +1
In this paper, we address the problem of adapting models from a source domain to a target domain, a task that has become increasingly important due to the brittle generalization of…
Learning Pose Image Manifolds Using Geometry-Preserving GANs and Elasticae
Shenyuan Liang, Pavan Turaga, Anuj Srivastava
This paper investigates the challenge of learning image manifolds, specifically pose manifolds, of 3D objects using limited training data. It proposes a DNN approach to manifold le…
Polynomial Implicit Neural Representations For Large Diverse Datasets
Rajhans Singh, Ankita Shukla, Pavan Turaga
Implicit neural representations (INR) have gained significant popularity for signal and image representation for many end-tasks, such as superresolution, 3D modeling, and more. Mos…
Leveraging Angular Distributions for Improved Knowledge Distillation
Eun Som Jeon, Hongjun Choi, Ankita Shukla +1
Knowledge distillation as a broad class of methods has led to the development of lightweight and memory efficient models, using a pre-trained model with a large capacity (teacher n…
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
Hongjun Choi, Eun Som Jeon, Ankita Shukla +1
Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustne…
Single-Shot Domain Adaptation via Target-Aware Generative Augmentation
Rakshith Subramanyam, Kowshik Thopalli, Spring Berman +2
The problem of adapting models from a source domain using data from any target domain of interest has gained prominence, thanks to the brittle generalization in deep neural network…
Domain Alignment Meets Fully Test-Time Adaptation
Kowshik Thopalli, Pavan Turaga, Jayaraman J. Thiagarajan
A foundational requirement of a deployed ML model is to generalize to data drawn from a testing distribution that is different from training. A popular solution to this problem is…
Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments
Rajhans Singh, Ankita Shukla, Pavan Turaga
Deep networks for image classification often rely more on texture information than object shape. While efforts have been made to make deep-models shape-aware, it is often difficult…
Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation
Kowshik Thopalli, Jayaraman J Thiagarajan, Rushil Anirudh +1
Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, subspace-based methods form an…
Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Anirudh Som, Ankita Shukla +3
Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number o…
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization
Kowshik Thopalli, Sameeksha Katoch, Pavan Turaga +1
Domain generalization (DG) methods aim to develop models that generalize to settings where the test distribution is different from the training data. In this paper, we focus on the…
Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
Ankita Shukla, Rushil Anirudh, Eugene Kur +5
In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion. Unlike a typical hypers…
Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics
John Kevin Cava, John Vant, Nicholas Ho +4
In this paper, we utilized generative models, and reformulate it for problems in molecular dynamics (MD) simulation, by introducing an MD potential energy component to our generati…
Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint
Ella Y. Wang, Anirudh Som, Ankita Shukla +2
Deep neural networks have increasingly been used as an auxiliary tool in healthcare applications, due to their ability to improve performance of several diagnosis tasks. However, t…
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization
Suhas Lohit, Rushil Anirudh, Pavan Turaga
Motion capture (mocap) and time-of-flight based sensing of human actions are becoming increasingly popular modalities to perform robust activity analysis. Applications range from a…
Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification
Hongjun Choi, Anirudh Som, Pavan Turaga
Standard deep learning models that employ the categorical cross-entropy loss are known to perform well at image classification tasks. However, many standard models thus obtained of…
Generative Patch Priors for Practical Compressive Image Recovery
Rushil Anirudh, Suhas Lohit, Pavan Turaga
In this paper, we propose the generative patch prior (GPP) that defines a generative prior for compressive image recovery, based on patch-manifold models. Unlike learned, image-lev…
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security
Ankita Shukla, Pavan Turaga, Saket Anand
Input transformation based defense strategies fall short in defending against strong adversarial attacks. Some successful defenses adopt approaches that either increase the randomn…
Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns
Anirudh Som, Narayanan Krishnamurthi, Matthew Buman +1
Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challeng…
Topological Descriptors for Parkinson's Disease Classification and Regression Analysis
Afra Nawar, Farhan Rahman, Narayanan Krishnamurthi +2
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper,…
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification
Hongjun Choi, Anirudh Som, Pavan Turaga
Deep-learning architectures for classification problems involve the cross-entropy loss sometimes assisted with auxiliary loss functions like center loss, contrastive loss and tripl…
Halluci-Net: Scene Completion by Exploiting Object Co-occurrence Relationships
Kuldeep Kulkarni, Tejas Gokhale, Rajhans Singh +2
Recently, there has been substantial progress in image synthesis from semantic labelmaps. However, methods used for this task assume the availability of complete and unambiguous la…
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning
Sameeksha Katoch, Kowshik Thopalli, Jayaraman J. Thiagarajan +2
Exploiting known semantic relationships between fine-grained tasks is critical to the success of recent model agnostic approaches. These approaches often rely on meta-optimization…
Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning
Ankita Shukla, Sarthak Bhagat, Shagun Uppal +2
Learning representations that can disentangle explanatory attributes underlying the data improves interpretabilty as well as provides control on data generation. Various learning f…