Atul Dhingra's repositories
Face-Recognition
In this thesis, we study two problems based on clustering algorithms. In the first problem, we study the role of visual attributes using an agglomerative clustering algorithm to whittle down the search area where the number of classes is high to improve the performance of clustering. We observe that as we add more attributes, the clustering performance increases overall. In the second problem, we study the role of clustering in aggregating templates in a 1:N open set protocol using multi-shot video as a probe. We observe that by increasing the number of clusters, the performance increases with respect to the baseline and reaches a peak, after which increasing the number of clusters causes the performance to degrade. Experiments are conducted using recently introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition datasets.
Python-Exercises
Python Questionnaire
Ethnicity-Classifier
This classifier uses the sub-strings of size 3 to learn probability associated with each substring such that it belongs to a particular ethnic group using the census 2000 data. When a new string is presented, it is broken down into sub-strings of size 3, and prediction is done on each of the sub-string using the model computed before. The output accuracy of the predictor is 85%. The model can be improved by balancing the sub-strings and the across the ethnic groups. The model follows the Naive Bayes assumption of conditional independence
ID-CGAN
Image De-raining Using a Conditional Generative Adversarial Network
SearchAlgorithms
Part of coursework for Intro to AI(CS-520) at Rutgers University
Classifiers
Project for CS-520(Intro to AI) at Rutgers University
AlgorithmSnippets
Visualize minimum spanning tree algorithm by taking input nodes from the user.