Sameer Ambekar

Sameer is working as Research Intern and pursuing Masters in Artificial Intelligence (MSc AI) at the University of Amsterdam (UvA). At UvA, he works under the guidance of Prof. Xiantong Zhen and Prof. Cees Snoek. At UvA, I am working on Test-Time Adaptation of classifiers (🔵->🟦->🔷), Meta learning and Domain Generalization for efficient multi source training and transferrable feautres.

Previously, Sameer worked as Research Assistant at IIT Delhi India under the guidance of Prof. Prathosh A.P. in the field of Domain Adaptation and Analysis of head and neck cancer Hisptopathological Images. Before working at IITD, Sameer worked at Indian Council of Medical Research (ICMR) as a Researcher under the guidance of Dr. Subarna Roy (Scientist G and Director ICMR-NITM), and Mr. Pramod Kumar (Scientist C).

I eagerly look forward to delving deeper into my passion for Deep learning and Computer Vision in my academic endeavors, and actively seeking internship opportunities.

Hobbies: Playing the Flute 🎵, Swimming 🏊, Photography 📷

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I am interested in computer vision and deep learning.

Specifically: Domain Generalization, Domain Adaptation, Meta learning, Medical Imaging

Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search
Prashant Pandey, Aayush Kumar Tyagi, Sameer Ambekar, Prathosh AP,

16th European Conference On Computer Vision, ECCV 2020 [Spotlight - Top 5% of the accepted papers]
project page / arXiv / ECCV Website / code

We cast the skin segmentation problem as that of target-independent Unsupervised Domain Adaptation (UDA) where we use the data from the Red-channel of the visible-range to develop skin segmentation algorithm on NIR images. We propose a method for target-independent segmentation where the ‘nearest-clone’ of a target image in the source domain is searched and used as a proxy in the segmentation network trained only on the source domain. We prove the existence of ‘nearest-clone’ and propose a method to find it through an optimization algorithm over the latent space of a Deep generative model based on variational inference.

SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANs
Sameer Ambekar, Ankit Ankit, Diego van der Mast, Mark Alence, Matteo Tafuro, Christos Athanasiadis

ECCV 2022 workshop VIPriors, 2022
arXiv / ECCV Website / code

We propose a novel work named SKDCGN that attempts knowledge transfer using Knowledge Distillation (KD). In our proposed architecture, each independent mechanism (shape, texture, background) is represented by a student 'TinyGAN' that learns from the pretrained teacher 'BigGAN'. We demonstrate the efficacy of the proposed method using state-of-the-art datasets such as ImageNet, and MNIST by using KD and appropriate loss functions. Moreover, as an additional contribution, our paper conducts a thorough study on the composition mechanism of the CGNs, to gain a better understanding of how each mechanism influences the classification accuracy of an invariant classifier.

[Re] Counterfactual Generative Networks
Ankit Ankit, Sameer Ambekar, Mark Alence, Baradwaj Varadharajan

ML Reproducibility Challenge 2021, 2021

In this academic project at UvA, the objective was to reproduce papers that have been accepted for publication at top conferences

Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images
Kartikeya Badola, Sameer Ambekar, Himanshu Pant, Sumit Soman, Anuradha Sura, Rajiv Narang, Suresh Chandra, Jayadeva,

arXiv, 2022

We introduce a state-of-the-art technique, termed as Twin Augmentation, for modifying popular pre-trained deep learning models. Twin Augmentation boosts the performance of a pre-trained deep neural network without requiring re-training. Experiments show, that across a multitude of classifiers, Twin Augmentation is very effective in boosting the performance of given pre-trained model for classification in imbalanced settings.

Computer-aided diagnosis (CAD) of world threatening diseases
Pramod Kumar, Sameer Ambekar*, Subarna Roy, Pavan Kunchur
Springer-Nature, 2019

Research / Work Experience
University of Amsterdam, AIM Lab - Research Intern (Deep learning, Computer Vision)
June 2022 - Present
Indian Institute of Technology Delhi (IITD), Delhi, India - Research Assistant (Deep learning, Computer Vision)
January 2019 - July 2021
Indian Council of Medical Research (ICMR) NITM Bioinformatics Division, Belgaum, India - Research Trainee
October 2017 - December 2018
DbCom Inc., New Jersey, USA - Remote Intern
June 2015 - December 2016
Recipient of DigiCosme Full Master Scholarship, Université Paris-Saclay, France.
Attended Oxford Machine Learning Summer School (OxML 2020), Deep Learning - University of Oxford
Attended PRAIRIE/MIAI PAISS 2021 Machine Learning Summer Learning - INRIA, Naver Labs
Attended Regularization Methods for Machine Learning 2021 (RegML 2021) - University of Genoa
As a part of my recreational activity, I like to play the Indian Flute.
Positions of Responsibility
Served as Charter Secretary of Rotaract Club of GIT
President of Rotaract Club of GIT

He makes good websites