Search Results for author:
Found 16 papers, 5 papers with code
Polynomial Implicit Neural Representations For Large Diverse Datasets
With much fewer training parameters and higher representative power, our approach paves the way for broader adoption of INR models for generative modeling tasks in complex domains.
Ranked #3 on
Image Generation
on ImageNet 128x128
Leveraging Angular Distributions for Improved Knowledge Distillation
AMD loss uses the angular distance between positive and negative features by projecting them onto a hypersphere, motivated by the near angular distributions seen in many feature extractors.
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
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 robustness of the trained model.
Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments
Deep networks for image classification often rely more on texture information than object shape.
Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data
In this paper, we report the results of a detailed study that compares and contrasts various common choices and some hybrid data augmentation strategies in KD based human activity analysis.
Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics
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 generative model.
Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion.
Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint
In addition to these findings, our work also presents a new application of the OS regularizer in healthcare, increasing the post-hoc interpretability and performance of deep learning models for COVID-19 classification to facilitate adoption of these methods in clinical settings.
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security
In this work, we propose a defense strategy that applies random image corruptions to the input image alone, constructs a self-correlation based subspace followed by a projection operation to suppress the adversarial perturbation.
Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning
Learning representations that can disentangle explanatory attributes underlying the data improves interpretabilty as well as provides control on data generation.
Primate Face Identification in the Wild
This loss of habitat has skewed the population of several non-human primate species like chimpanzees and macaques and has constrained them to co-exist in close proximity of human settlements, often leading to human-wildlife conflicts while competing for resources.
Geometry of Deep Generative Models for Disentangled Representations
We use several metrics to compare the properties of latent spaces of disentangled representation models in terms of class separability and curvature of the latent-space.
Unique Identification of Macaques for Population Monitoring and Control
Despite loss of natural habitat due to development and urbanization, certain species like the Rhesus macaque have adapted well to the urban environment.
Perturbation Robust Representations of Topological Persistence Diagrams
However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets.
Semi-Supervised Clustering with Neural Networks
Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications.
Matrix recovery using Split Bregman
The use of Bregman technique improves the convergence speed of our algorithm and gives a higher success rate.
