Search Results for author:
Found 18 papers, 1 papers with code
LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions
We then propose a neural network architecture for learning the linear implicit shape representation of the 3D surface of an object.
Deriving Explanation of Deep Visual Saliency Models
Then, we evaluate and compare the performance of the explainable models derived from UNISAL, MSI-Net and CMRNet on three benchmark datasets with other state-of-the-art methods.
Understanding Character Recognition using Visual Explanations Derived from the Human Visual System and Deep Networks
We have used eye-tracking to assay the spatial distribution of information hotspots for humans via fixation maps and an activation mapping technique for obtaining analogous distributions for deep networks through visualization maps.
Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images
Firstly, we present the COVID-19 Multi-Task Network which is an automated end-to-end network for COVID-19 screening.
Compressive sensing based privacy for fall detection
Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance.
Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept Drift
Data abundance along with scarcity of machine learning experts and domain specialists necessitates progressive automation of end-to-end machine learning workflows.
DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks
In this paper, we present a novel end-to-end coupled Denoising based Saliency Prediction with Generative Adversarial Network (DSAL-GAN) framework to address the problem of salient object detection in noisy images.
Mode matching in GANs through latent space learning and inversion
Generative adversarial networks (GANs) have shown remarkable success in generation of unstructured data, such as, natural images.
Unsupervised Conditional Generation using noise engineered mode matching GAN
Conditional generation refers to the process of sampling from an unknown distribution conditioned on semantics of the data.
Localization: A Missing Link in the Pipeline of Object Matching and Registration
These approaches require object-centric images to perform matching.
Unsupervised Despeckling
Contrast and quality of ultrasound images are adversely affected by the excessive presence of speckle.
Drought Stress Classification using 3D Plant Models
Quantification of physiological changes in plants can capture different drought mechanisms and assist in selection of tolerant varieties in a high throughput manner.
A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark
Training deep networks is expensive and time-consuming with the training period increasing with data size and growth in model parameters.
Ranking academic institutions on potential paper acceptance in upcoming conferences
The crux of the problem in KDD Cup 2016 involves developing data mining techniques to rank research institutions based on publications.
A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine
In this paper we propose to apply CNN to small data sets like for example, personal albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model.
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network.
Real World Applications of Machine Learning Techniques over Large Mobile Subscriber Datasets
Communication Service Providers (CSPs) are in a unique position to utilize their vast transactional data assets generated from interactions of subscribers with network elements as well as with other subscribers.
Improving Collaborative Filtering based Recommenders using Topic Modelling
While computing similarity between users, we make use of a combined similarity measure involving rating overlap as well as similarity in the latent topic space.
