Papers on Vision

• This repo was long due which consists all my re-implementations of various papers in computer vision that I read during the last few years.

• All papers follow this structure:

  • Paper Name
    • Original Paper
    • Code Reimplementation: Using PyTorch/Tensorflow(both in most papers)
    • Paper Summary with Report(in most papers)

• This repo will be updated whenever possible.

Papers List

• 2010

  • 01.Noise-contrastive estimation-A new estimation principle for Unnormalized Statistical Models : Paper || Code || Summary

• 2012

  • 02.AlexNet-ImageNet Classification with Deep Convolutional Neural Networks : Paper || Code || Summary
  • 03.3D Convolutional Neural Networks for Human Action Recognition : Paper || Code || Summary

• 2014

  • 04.Visualizing and Understanding Convolutional neural networks : Paper || Code || Summary
  • 05.Generative Adversarial Networks : Paper || Code || Summary
  • 07.Going Deeper with Convolutions : Paper || Code || Summary
  • 09.Distilling the Knowledge in a Neural Network : Paper || Code || Summary
  • 10.Network in Network : Paper || Code || Summary
  • 11.Intriguing properties of neural networks : Paper || Code || Summary

• 2015

  • 06. Very Deep Convolutional Networks for Large-Scale Image Recognition : Paper || Code || Summary
  • 08.FaceNet: A unified embedding for face recognition and clustering : Paper || Code || Summary

• 2016

  • 12.Rethinking the Inception Architecture for Computer Vision : Paper || Code || Summary
  • 13.Deep Residual Learning for Image Recognition : Paper || Code || Summary
  • 14.Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks : Paper || Code || Summary
  • 15.You Only Look Once: Unified, Real-Time Object Detection : Paper || Code || Summary

• 2017

  • 16.Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning : Paper || Code || Summary
  • 17.MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications : Paper || Code || Summary

• 2018

  • 18.Relational inductive biases, deep learning, and graph networks : Paper || Code || Summary

• 2019

  • 20.EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks : Paper || Code || Summary

• 2020

  • 21.Denoising Diffusion Probabilistic Models : Paper || Code || Summary

• 2021

  • 19.An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale : Paper || Code || Summary

Inspirations

• The key sources of inspiration for this repo were the works of Abhisek Das & Aman Chadha