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adl_vqa_tensorflow2

Project Submission for Applied Deep Learning - Spring 2019

aplusc98.github.io

portfolio

bicyclegan

Tensorflow implementation of the NIPS paper "Toward Multimodal Image-to-Image Translation"

cc-pyspark

Process Common Crawl data with Python and Spark

dl_project_psmnet

PSMNet Replication and Modification

keras-zero-shot-detection

Keras implementation of zero-shot detection based on YOLOv3 model.

mbem-keras

This repository gives a Keras implementation of the MBEM algorithm proposed in the paper Learning From Noisy Singly-labeled Data published at ICLR 2018. The original implementation in MXNet is given at https://github.com/khetan2/MBEM

munit-tensorflow

Simple Tensorflow implementation of "Multimodal Unsupervised Image-to-Image Translation" (ECCV 2018)

new-product-demand-prediction

This project involves in predicting the demand for a product with new features based on the historical data of demand available for similar and currently existing products using Machine learning algorithms such as Clustering, Regression. The model further uses a method for computing an normalized innovation potential factor for the new feature. This innovation potential can be used to predict the change in sales that can occur because of the addition of the new feature.

resizemultipleimages

Resize Multiple image files in a given Directory and sub directories in a linux based system.

siamese-networks-for-one-shot-learning

Implementation of Siamese Neural Networks for One-shot Image Recognition

siamesenetworks-facerecognition

siamesewordrecognition

HandWritten Word Recognition - Code and data

signature-verification-using-deep-learning

Using SigComp'11 dataset for signature verification

trajectoryprediction

Course project for CMU Visual Learning and Recognition, Spring 2021

zero-shot-learning

A python ZSL system which makes it easy to run Zero-Shot Learning on new datasets, by giving it features and attributes. Used for the paper "Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features", published in ICFHR2018.