Hello, I’m

Rishab Sharma


Isha Yoga Member
Himalayan Trekker
Competetive Gamer
(Click) My Age = new Date().getFullYear() - 1996;

Things I love

Deep Learning

I Love Deep Learning Research

Applied Machine Learning

I Love making Machine Learning Applications and Statistical Inference

Data and Model for Production

Experienced in Making Production grade Data and Model Piplines.

About Me

Hi, I am Rishab Sharma, an ambitious self-taught Artificial Intelligence Researcher and Engineer from India. I was born and raised in the Himalayan Valley of Dehradun. I am presently focused in applied deep learning and actively contributing to the research community.

I am presently working as a Data Scientist II for"Fynd Research".I also enjoy doing Model Architecture Designing and Production pipeline designing. Since I love both programming and research, I love making products using deep learning.
Fynd Trak ,Erase BG ,Fynd Now.

“Science is simple. Difficult is its application.“

- Rishab Sharma

“Never look down on someone. Never look up to someone.“

- Rishab Sharma

“Deep Learning and Machine Learning is not just a tech. Its's an art.”

- Rishab Sharma

Research Papers

AlphaNet- An Attention Guided Deep Network for Automatic Image Matting

In this paper, we propose an end to end solution for image matting i.e high-precision extraction of foreground objects from natural images. Image matting and background detection can be achieved easily through chroma keying in a studio setting when the background is either pure green or blue. Nonetheless, image matting in natural scenes with complex and uneven depth backgrounds remains a tedious task that requires human intervention. To achieve complete automatic foreground extraction in natural scenes, we propose a method that assimilates semantic segmentation and deep image matting processes into a single network to generate detailed semantic mattes for image composition task. The contribution of our proposed method is two-fold, firstly it can be interpreted as a fully automated semantic image matting method and secondly as a refinement of existing semantic segmentation models. We propose a novel model architecture as a combination of segmentation and matting that unifies the function of upsampling and downsampling operators with the notion of attention.

Employing Differentiable Neural Computers for Image Captioning and Neural Machine Translation

In the history of artificial neural networks, LSTMs have proved to be a high-performance architecture at sequential data learning. Although LSTMs are remarkable in learning sequential data but are limited in their ability to learn long-term dependencies and representation of certain data structures because of the lack of external memory. In this paper, we tackled two main tasks, one is language translation and other is image captioning. We approached the problem of language translation by leveraging the capabilities of the recently developed DNC architectures. Here we modified the DNC architecture by including dual neural controllers instead of one and an external memory module. Inside our controller, we employed a neural network with memory-augmentation which differs from the original differentiable neural computer, we implemented a dual controller’s system in which one controller is for encoding the query sequence whereas another controller is for decoding the translated sequences.

Retrieving Similar E-Commerce Images Using Deep Learning

In this paper, we propose a deep convolutional neural network for learning the embeddings of images in order to capture the notion of visual similarity. We present a deep siamese architecture that when trained on positive and negative pairs of images learn an embedding that accurately approximates the ranking of images in order of visual similarity notion.


Product Devlopment Intern

Development of Machine Learning Apps on Hasura Platform

Data Scientist II - Fynd

Fynd Research Team - Worked on Image Matting, Image similarity, Multi-object Tracking, Key-point Detection

Deep Learning Researcher TREES - ISRO

Space Application Center - Worked On Neural Architecture Search, One Shot Learning, Semantic Segmentation, NDVI, H-NAS, Morph-Net, NNI, Lottery Ticket Hypothesis


Robotics - LFR, Counter, Switch, Keyboard Robo

Neural Turing Machine in Pytorch

Nidaan - Disease Recognition

OCR on Android

Self Driving Car Simulation using simple CNN

Chatbot - AIML + TF

Snack Search - ML Recommender

Cross a Crater - IITB - Eyantra

Pratyeti - ML and Blockchain based Navigation System

Dressopedia - Deep Learning App

University Hub - NodeJs App

Modi Vs Kejriwal

Latex for One page Two Coloumn PDF

AIML Based Chatbot

Lua Non-threaded Scripts for V-rep


Hackathon App

Trial Geospatial Plotting App

OCR - Tesseract


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Thank You

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