Search Results for author: Shivang Agarwal

Found 5 papers, 0 papers with code

EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection

no code implementations3 Mar 2021 Shivang Agarwal, C. Ravindranath Chowdary, Vivek Sourabh

EaZy learning is similar to ensemble learning as it generates an ensemble of base classifiers and integrates them to make a prediction.

Ensemble Learning

AILearn: An Adaptive Incremental Learning Model for Spoof Fingerprint Detection

no code implementations29 Dec 2020 Shivang Agarwal, Ajita Rattani, C. Ravindranath Chowdary

AILearn is an adaptive incremental learning model which adapts to the features of the ``live'' and ``spoof'' fingerprint images and efficiently recognizes the new spoof fingerprints as well as the known spoof fingerprints when the new data is available.

Incremental Learning

EILearn: Learning Incrementally Using Previous Knowledge Obtained From an Ensemble of Classifiers

no code implementations8 Feb 2019 Shivang Agarwal, C. Ravindranath Chowdary, Shripriya Maheshwari

In incremental learning, the general convention is to use only the knowledge acquired in the previous phase but not the previously seen data.

Incremental Learning

Structuring an unordered text document

no code implementations29 Jan 2019 Shashank Yadav, Tejas Shimpi, C. Ravindranath Chowdary, Prashant Sharma, Deepansh Agrawal, Shivang Agarwal

Segmenting an unordered text document into different sections is a very useful task in many text processing applications like multiple document summarization, question answering, etc.

Document Summarization Question Answering +1

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

no code implementations10 Sep 2018 Shivang Agarwal, Jean Ogier du Terrail, Frédéric Jurie

Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e. g., 'car', 'plane', etc.)

Object object-detection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.