Asish Chakrapani's repositories
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.
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
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
Siamese-Networks-for-One-Shot-Learning
Implementation of Siamese Neural Networks for One-shot Image Recognition
SiameseWordRecognition
HandWritten Word Recognition - Code and data
keras-zero-shot-detection
Keras implementation of zero-shot detection based on YOLOv3 model.
MUNIT-Tensorflow
Simple Tensorflow implementation of "Multimodal Unsupervised Image-to-Image Translation" (ECCV 2018)
Resizemultipleimages
Resize Multiple image files in a given Directory and sub directories in a linux based system.
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.