Variational autoencoders can discover unique ways to encode-decode the input from a distribution. Bonus: It can generate images!
Back in college, I did a class project where I used computer vision techniques for the first time. It was the age before deep learning. Today, I want to revisit this old project.
An uploaded family photo inspired me to pick up a colorizer tool. Here is my post honoring my family’s history.
In this video, I’ll discuss my solution to the SIIM-ISIC Melanoma Classification Challenge hosted in Kaggle.
Hi all! I’m trying out this new format of publishing my projects through Powerpoint videos. Recording this way is fun. Let me know if this is a better format for you. This pet project is my tribute to books. As a machine learning person, I’d like to see if I can artificially create images of […]
If a computer would have eyes, what would it be able to recognize? Distinguishing cats and dogs would be nice, but what’s better is recognizing all 7,870 objects in the Open Images dataset!
Time for another vision blog. Here, I’ll talk about dogs. Humans can identify a dog from a cat well enough, but it’s a little harder to distinguish specific dog breeds. For instance, can you tell which is which in the following? Well, on the left is a Siberian Husky and on the right is an […]
This is quite a late post for me, but I just want to detail here the session I taught three weeks ago. We have a large machine learning competition in Trend Micro and I volunteered to hold a boot camp for Trend Labs, the “Philippine division”, prior to the competition. Thinking that it was high […]
Another interesting thing about deep learning is, being inspired by neural networks, it is similar to how our brains work. Our brains have different areas for different stimuli, and our neurons combine these signals hierarchically.
For this post, I’m sharing my presentation in our computer vision class. We are given two papers with similar topics and we are to discuss them in 15 minutes.