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Aadarsh Sahoo's Projects

al-go-rithms icon al-go-rithms

:musical_note: Algorithms written in different programming languages - https://zoranpandovski.github.io/al-go-rithms/

apollo-11 icon apollo-11

Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.

ba3us icon ba3us

code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

clrs icon clrs

:notebook:Solutions to Introduction to Algorithms

compilers icon compilers

Compilers Course lab assignments for sem 5 at IIT Kharagpur (Tiny C)

convnet-aig icon convnet-aig

PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

cs231n icon cs231n

My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition

dda icon dda

Code release for "Dynamic Domain Adaptation for Efficient Inference" (CVPR 2021)

deepcoral icon deepcoral

A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

detectron icon detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

dsbn icon dsbn

Official Implementation of "Domain Specific Batch Normalization for Unsupervised Domain Adaptation (CVPR2019)"

fixmatch icon fixmatch

A simple method to perform semi-supervised learning with limited data.

fortunealgo icon fortunealgo

C++ implementation of Fortune algorithm for Voronoi diagram construction

imbalancecyclegan icon imbalancecyclegan

This repository contains code for the paper "Mitigating Dataset Imbalance via Joint Generation and Classification", published at ECCV-2020 Workshop on Imbalance Problems in Computer Vision (IPCV).

imgaug icon imgaug

Image augmentation for machine learning experiments.

mm-sada_domain_adaptation_splits icon mm-sada_domain_adaptation_splits

This repository contains the annotations used for evaluating Unsupervised Domain Adaptation on EPIC Kitchens, with individual kitchens used as seperate domains. The experimental setup can be found in the Multi-modal Domain Adaptation for Fine-Grained Action Recognition (MM-SADA) publication.

mnist-m icon mnist-m

The MNIST-M dataset for domain adaptation (PyTorch).

pintos-project2 icon pintos-project2

Adding user programs support to Pintos kernel: loading executables / System calls handling Mechanism / IPC

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