WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Projects
- KontaKt App
- Sudoku Spoiler
- GLSL Raytracing
- Decentralized Land Registration on Ethereum
- Galactic Collision Simulator
- Competetive coding
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Generating a human pose dataset using PC games
- Monte Carlo Path Tracing Renderer
- Tabbing App
- Geo-location Augmented Reality
- AR chess app
- Prevention of Sophisticated DoS attack / Network Security
- Winning a Deep Learning challenge
- Automated Fiducial Localisation from MRI/CT Images
- Panorama in Cam Scanner
- 3D Object Reconstruction from Single Image
- Statistical Modelling of Star Maps
- Face Recognition Systems
- Competitive Coding
- Can Machines Identify Genres?
- Joint Modelling of Source Code and Natural Language
- Front end development for FOSSEE websites
- Institute Delivery System
- Capture The Swag
- Panorama in Cam-Scanner
- Poisson Solver with Image Editing
- Blind Source Separation
- FAQ Bot for Freshmen
- Capturing semantic structures in Neural Machine Translation
The project will be a implementation of the paper A Point Set Generation Network for 3D Object Reconstruction from a Single Image
The code is available at: https://github.com/fanhqme/PointSetGeneration. Since the code is done using Tensorflow, this will try to replicate the results using Pytorch. A bonus part of the project would be to try to remove a major constraint imposed by the author.
Hard prerequisite: Prior experience in Python coding. Everything else can be learned on the fly if the student is motivated enough. A course in Probability Theory is also very much recommended.
Soft Pre-requisite: Prior experience in Pytorch or any other deep learning library (can be learnt on the go if mentee is motivated). Basic 3D vision concepts.
|Week 1-2 :||Cover up existing pre-requisites, thorough reading of the paper, and a discussion among the group and me. Getting a template project running.|
|Week 3-4 :||Code parts of the algorithm and check each part individually if they are training.|
|Week 5-7 :||Hyperparameter tuning and debugging the models. This is expected to take the major chunk of time. The bonus part will be done only if this part is completed within 1.5 weeks.|
|Week 8 :||Documenting everything that has been done properly (note documentation will be an ongoing process, this will just be wrapping up everything).|
Expected amount of hours mentees need to spend: In short depends. Coding in a new library can often be daunting and it may require more than one nightouts. Average 10-15 hours in a week, might be higher in some particular cases.
Resources required: Google GPUs will be used for this purpose. Google gives $300 credits for first time users.