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Catherine Ordun

Catherine Ordun

PhD Student at UMBC - Healthcare AI and Computer Vision

📍 Washington, D.C.

Easy Statistics for Model Evaluation

Statistics is the tried and true way of evaluating the performance of a machine learning model. I wrote the material below about two years ago when I was in the thick of my PhD. It's a basic overview of many terms you'll hear over and over

Taking a look at "How do Deepfakes Move?"

I read an excellent paper this morning by Demir from Intel and Ciftci from Binghamtom University entitled How do Deepfakes Move? Motion Magnification for Deepfake Source Detection, published at CVPR. They also published FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals. The paper posits that by analyzing features of

Detecting Deep Fakes

I don't have to tell you that everything we see online nowadays is increasingly suspicious. In our age of Generative AI and jaw dropping Tik Tok filters, it's hard to know what's real or what's fake. Since my dissertation was focused on

Can Language Agents Play RPGs?

I'm getting back into it after a holiday break and one of the most fascinating areas of AI research is Language Agents. I'm more of a computer vision person, so LLMs are not my bread and butter. But, I've grown increasingly interested in the

Lessons Learned as PhD Student

Before I defend, I wanted to share my Top 5 Lessons Learned as a PhD Student. If you're thinking about applying for a PhD - especially in computer science (or adjacent) fields - these might pertain to you. However, I think they're relevant for most PhD experiences.

Centroids and Large Persons in Yolov5

I remember when pjreddie released You-Only-Look-Once (YOLO) several years ago, and installing darknet (https://github.com/pjreddie/darknet). I also laughed when I read his hilarious licensing agreement LICENSE.fuck (https://github.com/pjreddie/darknet/blob/master/LICENSE.fuck). Well, Yolo continues to give back. And now in 2022 we

PyTorch Pair-Wise Transforms and Data

Consider that you have a pair of images you want to train. In my case, it's a GAN, where I'm improving upon older work from 2020 in the facial-visible-to-thermal GAN [https://github.com/nudro/favtgan]. My paired image consists of a visible and thermal face, simultaneously

Basic cropping with PIL and PyTorch

I'm embarrassed to say that even at this point in my life, I still have to look up this formatting to crop an image. Once and for all, I'll post it here so that I don't forget it and also in case you'

Basics of understanding someone else's AI code

You'll eventually face a moment where you're going to have to read through an (at-first) complicated set of AI code. You might need to leverage another expert's repo in order to build an experiment. You might be in a company, and found a great

Causality in GANs

This semester I'm taking a course at UMBC called Causality in Machine Learning by Dr. Osman Gani [https://osmanii.github.io/]. So far, we've studied Simpson's Paradox, Causality and the Potential Outcomes Framework, Average Treatment Effect, Ignorability, Exchangability, Identifiability, Consistency, Structural Causal Models (i.