Search Results for author:
Found 8 papers, 1 papers with code
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning
The rapid proliferation of digital content and the ever-growing need for precise object recognition and segmentation have driven the advancement of cutting-edge techniques in the field of object classification and segmentation.
Unsupervised learning based object detection using Contrastive Learning
Training image-based object detectors presents formidable challenges, as it entails not only the complexities of object detection but also the added intricacies of precisely localizing objects within potentially diverse and noisy environments.
Pragmatic Comparison Analysis of Alternative Option Pricing Models
In this paper, we price European Call three different option pricing models, where the volatility is dynamically changing i. e. non constant.
Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)
We propose a deep learning methodology for multivariate regression that is based on pattern recognition that triggers fast learning over sensor data.
HDRVideo-GAN: Deep Generative HDR Video Reconstruction
We first extract clean LDR frames from noisy LDR video with alternating exposures with a denoising network trained in a self-supervised setting.
Bayesian Optimization -- Multi-Armed Bandit Problem
In this report, we survey Bayesian Optimization methods focussed on the Multi-Armed Bandit Problem.
Efficient Object Detection Model for Real-Time UAV Applications
We propose a deep feature pyramid architecture which makes use of inherent properties of features extracted from Convolutional Networks by capturing more generic features in the images (such as edge, color etc.)
A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS
Our work is mainly focused on autonomous flying drones where we establish a case study towards efficiency, robustness and accuracy of UAVs where we showed our results well supported through experiments.
