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05/17/2023
Learning Pose Image Manifolds Using Geometry-Preserving GANs and Elasticae
This paper investigates the challenge of learning image manifolds, speci...
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03/20/2023
Polynomial Implicit Neural Representations For Large Diverse Datasets
Implicit neural representations (INR) have gained significant popularity...
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02/27/2023
Leveraging Angular Distributions for Improved Knowledge Distillation
Knowledge distillation as a broad class of methods has led to the develo...
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11/08/2022
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
Mixup is a popular data augmentation technique based on creating new sam...
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05/24/2022
Deep Geometric Moment
Deep networks for image classification often rely more on texture inform...
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01/01/2022
Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data
Deep neural networks are parametrized by several thousands or millions o...
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11/28/2021
Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics
In this paper, we utilized generative models, and reformulate it for pro...
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12/03/2020
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization
Motion capture (mocap) and time-of-flight based sensing of human actions...
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09/22/2020
Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification
Standard deep learning models that employ the categorical cross-entropy ...
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06/18/2020
Generative Patch Priors for Practical Compressive Image Recovery
In this paper, we propose the generative patch prior (GPP) that defines ...
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05/06/2020
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security
Input transformation based defense strategies fall short in defending ag...
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05/06/2020
Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns
Application and use of deep learning algorithms for different healthcare...
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04/21/2020
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification
Deep-learning architectures for classification problems involve the cros...
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04/18/2020
Halluci-Net: Scene Completion by Exploiting Object Co-occurrence Relationships
We address the new problem of complex scene completion from sparse label...
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04/15/2020
Topological Descriptors for Parkinson's Disease Classification and Regression Analysis
At present, the vast majority of human subjects with neurological diseas...
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11/24/2019
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning
Exploiting known semantic relationships between fine-grained tasks is cr...
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07/22/2019
Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning
Learning representations that can disentangle explanatory attributes und...
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06/13/2019
Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping
Many time-series classification problems involve developing metrics that...
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06/11/2019
SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation
Unsupervised domain adaptation aims to transfer and adapt knowledge lear...
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06/05/2019
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
Topological features such as persistence diagrams and their functional a...
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05/16/2019
Non-Parametric Priors For Generative Adversarial Networks
The advent of generative adversarial networks (GAN) has enabled new capa...
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02/19/2019
Geometry of Deep Generative Models for Disentangled Representations
Deep generative models like variational autoencoders approximate the int...
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12/20/2018
An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation
Three-dimensional (3D) biomedical image sets are often acquired with in-...
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11/11/2018
Multiple Subspace Alignment Improves Domain Adaptation
We present a novel unsupervised domain adaptation (DA) method for cross-...
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09/08/2018
Rate-Adaptive Neural Networks for Spatial Multiplexers
In resource-constrained environments, one can employ spatial multiplexin...
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07/26/2018
Perturbation Robust Representations of Topological Persistence Diagrams
Topological methods for data analysis present opportunities for enforcin...
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06/08/2018
CS-VQA: Visual Question Answering with Compressively Sensed Images
Visual Question Answering (VQA) is a complex semantic task requiring bot...
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02/05/2018
Compressive Light Field Reconstructions using Deep Learning
Light field imaging is limited in its computational processing demands o...
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10/29/2016
Diversity Promoting Online Sampling for Streaming Video Summarization
Many applications benefit from sampling algorithms where a small number ...
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05/28/2016
A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams
Topological data analysis is becoming a popular way to study high dimens...
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03/16/2016
Persistent Homology of Attractors For Action Recognition
In this paper, we propose a novel framework for dynamical analysis of hu...
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03/07/2016
Elastic Functional Coding of Riemannian Trajectories
Visual observations of dynamic phenomena, such as human actions, are oft...
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01/27/2016
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference
This paper presents a shape-theoretic framework for dynamical analysis o...
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01/27/2016
Fast Integral Image Estimation at 1
We propose a framework called ReFInE to directly obtain integral image e...
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01/26/2016
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
The goal of this paper is to present a non-iterative and more importantl...
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01/18/2015
Reconstruction-free action inference from compressive imagers
Persistent surveillance from camera networks, such as at parking lots, U...
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06/21/2014