publications

2022

1. 

   Astronomical Image Colorization and Up-scaling with Conditional Generative Adversarial Networks

   Shreyas Kalvankar, Hrushikesh Pandit, Pranav Parwate, and 2 more authors

   2022

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   This research aims to provide an automated approach for the problem of Image colorization and Single Image Super Resolution by focusing on a very specific domain: astronomical images, using Generative Adversarial Networks. We explore the usage of various models in RBG and L*a*b color spaces. We use transfer learning owing to a small data set, using pre-trained ResNet-18 as a backbone encoder and fine-tune it further. The model produces visually appealing images that are high resolution and colorized. We present our results by evaluating the GANs quantitatively using distance metrics such as L1 distance and L2 distance in each of the color spaces across all channels to provide a comparative analysis. We use Fréchet inception distance (FID) to compare the distribution of the generated images and real image to assess the model’s performance.

   @article{kalvankar2022astronomical,
     title = {Astronomical Image Colorization and Up-scaling with Conditional Generative Adversarial Networks},
     author = {Kalvankar, Shreyas and Pandit, Hrushikesh and Parwate, Pranav and Patil, Atharva and Kamalapur, Snehal},
     year = {2022},
     publisher = {Gesellschaft f{\"u}r Informatik, Bonn},
   }

2020

1. 

   EinsteinPy: A Community Python Package for General Relativity

   Shreyas Bapat, Ritwik Saha, Bhavya Bhatt, and 46 more authors

   2020

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   EinsteinPy performs General Relativity and gravitational physics tasks, including geodesics plotting for Schwarzschild, Kerr and Kerr Newman space-time models, calculation of Schwarzschild radius, and calculation of event horizon and ergosphere for Kerr space-time. It can perform symbolic manipulations of various tensors such as Metric, Riemann, Ricci and Christoffel symbols. EinsteinPy also features hypersurface embedding of Schwarzschild space-time, and includes other utilities and functions. It is a community-developed package and is written in Python.

   @misc{bapat2020einsteinpy,
     title = {EinsteinPy: A Community Python Package for General Relativity},
     author = {Bapat, Shreyas and Saha, Ritwik and Bhatt, Bhavya and Jain, Shilpi and Jain, Akshita and Vela, Sofía Ortín and Khandelwal, Priyanshu and Shivottam, Jyotirmaya and Ma, Jialin and Ng, Gim Seng and Kerhalkar, Pratyush and Sarode, Hrishikesh Sudam and Sharma, Rishi and Gupta, Manvi and Gupta, Divya and Tyagi, Tushar and Rustagi, Tanmay and Singh, Varun and Bansal, Saurabh and Tayal, Naman and Manhas, Abhijeet and Reyna, Raphael and Kumar, Gaurav and Dixit, Govind and Kumar, Ratin and Mishra, Sashank and Jamgade, Alpesh and Singh, Raahul and Sanjay, Rohit and Shaikh, Khalid and Vidyarthi, Bhavam and K, Shamanth R Nayak and Gandham, Vineet and Vashistha, Nimesh and Das, Arnav and Saurabh and Kalvankar, Shreyas and Tarone, Ganesh and Mangat, Atul and Garg, Suyog and Gautam, Bibek and Srinivasan, Sitara and Gautam, Aayush and Singh, Swaastick Kumar and Salampuria, Suyash and Yauney, Zac and Gupte, Nihar and Shenoy, Gagan and Chan, Micky Yun},
     year = {2020},
     eprint = {2005.11288},
     archiveprefix = {arXiv},
     primaryclass = {gr-qc},
   }

2. 

   Galaxy Morphology Classification using EfficientNet Architectures

   Shreyas Kalvankar, Hrushikesh Pandit, and Pranav Parwate

   2020

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   We study the usage of EfficientNets and their applications to Galaxy Morphology Classification. We explore the usage of EfficientNets into predicting the vote fractions of the 79,975 testing images from the Galaxy Zoo 2 challenge on Kaggle. We evaluate this model using the standard competition metric i.e. rmse score and rank among the top 3 on the public leaderboard with a public score of 0.07765. We propose a fine-tuned architecture using EfficientNetB5 to classify galaxies into seven classes - completely round smooth, in-between smooth, cigarshaped smooth, lenticular, barred spiral, unbarred spiral and irregular. The network along with other popular convolutional networks are used to classify 29,941 galaxy images. Different metrics such as accuracy, recall, precision, F1 score are used to evaluate the performance of the model along with a comparative study of other state of the art convolutional models to determine which one performs the best. We obtain an accuracy of 93.7% on our classification model with an F1 score of 0.8857. EfficientNets can be applied to large scale galaxy classification in future optical space surveys which will provide a large amount of data such as the Large Synoptic Space Telescope.

   @misc{kalvankar2020galaxy,
     title = {Galaxy Morphology Classification using EfficientNet Architectures},
     author = {Kalvankar, Shreyas and Pandit, Hrushikesh and Parwate, Pranav},
     year = {2020},
     eprint = {2008.13611},
     archiveprefix = {arXiv},
     primaryclass = {cs.CV},
   }