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Paper notes

This repository contains my paper reading notes on deep learning and machine learning. It is inspired by Denny Britz and Daniel Takeshi.

New year resolution for 2020: read at least three paper a week and a high a high quality github repo a month!

What to read

Where to start?

If you are new to deep learning in computer vision and don't know where to start, I suggest you spend your first month or so dive deep into this list of papers. I did so (see my notes) and it served me well.

Here is a list of trustworthy sources of papers in case I ran out of papers to read.

Github repos

Youtube channels

Talks

My Review Posts by Topics

I regularly update my blog in Toward Data Science.

CVPR 2021

2021-04 (5)

2021-06 (1)

2021-03 (4)

2021-02 (0)

2021-01 (7)

2020-12 (17)

2020-11 (18)

2020-10 (14)

2020-09 (15)

2020-08 (26)

2020-07 (25)

2020-06 (20)

2020-05 (19)

2020-04 (14)

2020-03 (15)

2020-02 (12)

2020-01 (19)

2019-12 (12)

2019-11 (20)

2019-10 (18)

2019-09 (17)

2019-08 (18)

2019-07 (19)

2019-06 (12)

2019-05 (18)

2019-04 (12)

2019-03 (19)

2019-02 (9)

2019-01 (10)

2018

2017 and before

Papers to Read

Here is the list of papers waiting to be read.

Deep Learning in general

Self-training

2D Object Detection and Segmentation

Fisheye

Video Understanding

Pruning and Compression

Architecture Improvements

Reinforcement Learning

3D Perception

Stereo and Flow

Traffic light and traffic sign

Datasets and Surveys

Unsupervised depth estimation

Indoor Depth

lidar

Egocentric bbox prediction

Lane Detection

Tracking

keypoints: pose and face

General DL

Mono3D

Radar Perception

SLAM

Radar Perception

Reviews and Surveys

Beyond Perception in Autonomous Driving

Prediction and Planning

Annotation and Tooling

Non-DL

Technical Debt


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