# Where Are Pixels? -- a Deep Learning Perspective

Technically, an image is a function that maps a continuous domain, e.g. a box , to intensities such as (R, G, B). To store it on computer memory, an image is discretized to an array array[H][W], where each element array[i][j] is a pixel.

How does discretization work? How does a discrete pixel relate to the abstract notion of the underlying continuous image? These basic questions play an important role in computer graphics & computer vision algorithms.

This article discusses these low-level details, and how they affect our CNN models and deep learning libraries. If you ever wonder which resize function to use or whether you should add/subtract 0.5 or 1 to some pixel coordinates, you may find answers here. Interestingly, these details have contributed to many accuracy improvements in Detectron and Detectron2.

# Deep Learning Experiments and Claims

## 实验与 claims

Ross ICCV 2019 tutorial 最后谈了谈怎么写 paper. 第 126 页说, 文章中所有的 claim, 理想情况下都应该要么是文献中已有的 claim, 要么是有实验能够证明的 claim.

# Fight Against Silent Bugs in Deep Learning Libraries

TL;DR: How to find out if your favorite deep learning library is occasionally giving you wrong results? Such bugs happen from time to time, and are extremely difficult to notice, report, and debug.

# Unawareness of Deep Learning Mistakes

TL;DR: People are hardly aware of any deep learning mistakes they made, because things always appear to work, and there are no expectations on how well they should work. The solution is to try to accurately reproduce settings & performance of high-quality papers & code.

# OpenPano: How to write a Panorama Stitcher

This is a summary of the algorithms I used to write OpenPano: an open source panorama stitcher. You can find the source code on github.