Vanika Singhal
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View article: One-shot Localization and Segmentation of Medical Images with Foundation Models
One-shot Localization and Segmentation of Medical Images with Foundation Models Open
Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models with their ability to capture rich semantic features of the image have been used for image correspondence tasks on natural images. In this paper, we examine the …
View article: THE IDEAL OF KALOKAGATHIA OF THE TOTALITARIAN AND LIBERAL REGIMES AS A MEANS OF PERSONALITY FORMATION
THE IDEAL OF KALOKAGATHIA OF THE TOTALITARIAN AND LIBERAL REGIMES AS A MEANS OF PERSONALITY FORMATION Open
The object of research is the relationship between the state and the individual under totalitarian and liberal regimes. Investigated problem: in the article the concepts of totalitarian and liberal regime is analyzed, their nature and rela…
View article: Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral\n Image Classification
Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral\n Image Classification Open
In recent studies in hyperspectral imaging, biometrics and energy analytics,\nthe framework of deep dictionary learning has shown promise. Deep dictionary\nlearning outperforms other traditional deep learning tools when training data\nis l…
View article: Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification
Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification Open
In recent studies in hyperspectral imaging, biometrics and energy analytics, the framework of deep dictionary learning has shown promise. Deep dictionary learning outperforms other traditional deep learning tools when training data is limi…
View article: Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification Open
This work proposes a new framework for deep learning that has been particularly tailored for hyperspectral image classification. We learn multiple levels of dictionaries in a robust fashion. The last layer is discriminative that learns a l…
View article: Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning
Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning Open
Currently there are several well-known approaches to non-intrusive appliance load monitoring rule based, stochastic finite state machines, neural networks and sparse coding. Recently several studies have proposed a new approach based on mu…
View article: Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning
Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning Open
The concept of deep dictionary learning has been recently proposed. Unlike shallow dictionary learning which learns single level of dictionary to represent the data, it uses multiple layers of dictionaries. So far, the problem could only b…
View article: Influence of the education level of the population on the human development index
Influence of the education level of the population on the human development index Open
The questions connected with the concept of human development are analyzed, the basic idea of the concept of human development is considered, the connection between human development and indicators of economic growth is investigated. The m…
View article: Solving Inverse Problems in Imaging via Deep Dictionary Learning
Solving Inverse Problems in Imaging via Deep Dictionary Learning Open
In dictionary learning-based inversion, the dictionary and coefficients are learnt adaptively from the image during the inversion process; this is a shallow approach since one layer of the dictionary is learnt. This is the first work which…
View article: Correction to “Semi-Supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals From Compressive Measurements”
Correction to “Semi-Supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals From Compressive Measurements” Open
In the above paper [1], the funding information should read “This work was supported by the NPRP Grant 7–684–1–127 from the Qatar National Research Fund (a member of Qatar Foundation).”
View article: Semi-Supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals From Compressive Measurements
Semi-Supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals From Compressive Measurements Open
In this paper, the objective is to classify biomedical signals from their compressive measurements. The problem arises when compressed sensing (CS) is used for energy efficient acquisition and transmission of such signals for wireless body…
View article: How to Train Your Deep Neural Network with Dictionary Learning
How to Train Your Deep Neural Network with Dictionary Learning Open
Currently there are two predominant ways to train deep neural networks. The first one uses restricted Boltzmann machine (RBM) and the second one autoencoders. RBMs are stacked in layers to form deep belief network (DBN); the final represen…
View article: Deep Blind Compressed Sensing
Deep Blind Compressed Sensing Open
This work addresses the problem of extracting deeply learned features directly from compressive measurements. There has been no work in this area. Existing deep learning tools only give good results when applied on the full signal, that to…