How to spot fake images of faces generated by a GAN? Look at the eyes! πŸ‘οΈ

This is an interesting paper that shows how fake images of faces can be easily detected by looking at the shape of the pupil.

The pupils in GAN-generated images are usually not round - see the image!

πŸ‘‡
Here is the actual paper. The authors propose a way to automatically identify fake images by analyzing the pupil's shape.

arxiv.org/abs/2109.00162
The bad thing is, GANs will probably quickly catch up and include an additional constraint for pupils to be round...
So true! Do you know what this cat and mouse game reminds me of - a GAN... πŸ˜†

Yes, I also thought about that. What they do in the paper is essentially engineer a feature that does this, so it should definitely be possible for the discriminant to find it as well.

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More from @haltakov

21 Sep
There are two problems with ROC curves

❌ They don't work for imbalanced datasets
❌ They don't work for object detection problems

So what do we do to evaluate our machine learning models properly in these cases?

We use a Precision-Recall curve.

Another one of my threads πŸ‘‡
Last week I wrote another detailed thread on ROC curves. I recommend that you read it first if you don't know what they are.



Then go on πŸ‘‡
❌ Problem 1 - Imbalanced Data

ROC curves measure the True Positive Rate (also known as Accuracy). So, if you have an imbalanced dataset, the ROC curve will not tell you if your classifier completely ignores the underrepresented class.

More details:

πŸ‘‡
Read 17 tweets
15 Sep
Did you ever want to learn how to read ROC curves? πŸ“ˆπŸ€”

This is something you will encounter a lot when analyzing the performance of machine learning models.

Let me help you understand them πŸ‘‡
What does ROC mean?

ROC stands for Receiver Operating Characteristic but just forget about it. This is a military term from the 1940s and doesn't make much sense today.

Think about these curves as True Positive Rate vs. False Positive Rate plots.

Now, let's dive in πŸ‘‡
The ROC curve visualizes the trade-offs that a binary classifier makes between True Positives and False Positives.

This may sound too abstract for you so let's look at an example. After that, I encourage you to come back and read the previous sentence again!

Now the example πŸ‘‡
Read 21 tweets
14 Sep
Most people seem to use matplotlib as a Python plotting library, but is it really the best choice? πŸ€”

We are going to compare 5 free and popular libraries:
β–ͺ️ Matplotlib
β–ͺ️ Seaborn
β–ͺ️ Plotly
β–ͺ️ Bokeh
β–ͺ️ Altair

Which one is the best? Find out below πŸ‘‡
In a survey I did the other day, matplotlib had the most users by a large margin. This was quite surprising to me since I don't really like it...



But let's first look at each library πŸ‘‡
Matplotlib πŸ“ˆ

Matplotlib is one of the most popular libraries out there.

βœ… Supports many types of plots
βœ… Lots of customization options

❌ Plots look ugly
❌ Limited interactivity
❌ Not very intuitive to use
Read 11 tweets
9 Sep
I highly recommend listening to the latest eposide of @therobotbrains podcast with @ilyasut.

therobotbrains.ai/podcasts/episo…

Here are some insights I found particulalry interesting πŸ‘‡
"Neural networks are parallel computers"

That is why they are so powerful - you can train a generic computer to solve your problem. This is also the driver behind Software 2.0 - neural network are becoming more and more capable of solving all kinds of problems.
"Neural networks perform well on tasks that humans can perform very quickly"

Humans don't think much when listening, observing or performing simple tasks.

This means that a neural network can be trained to be good at it as well: NLP, computer vision and reinforcement learning.
Read 4 tweets
9 Sep
My setup for recording videos for my machine learning course πŸŽ₯

A lot of people asked about my setup the other day, so here a short thread on that. It's nothing fancy, but it does a good job πŸ€·β€β™‚οΈ

Details πŸ‘‡
Hardware βš™οΈ

β–ͺ️ MacBook Pro (2015 model) - screen sharing and recording
β–ͺ️ iPhone XS - using the back camera for video recording
β–ͺ️ Omnidiretional external mic - connected to the iPhone
β–ͺ️ Highly professional camera rig - books mostly about cooking and travel πŸ˜„

πŸ‘‡
Software πŸ’»

β–ͺ️ OBS Studio - recording of the screen and the camera image
β–ͺ️ EpocCam - use your iPhone as a web cam. You can connect your iPhone both over WiFi and cable.
β–ͺ️ Google Slides - for presentation
β–ͺ️ Jupyter notebooks and Google Colab - for experimenting with code

πŸ‘‡
Read 5 tweets
7 Sep
Let's talk about a common problem in ML - imbalanced data βš–οΈ

Imagine we want to detect all pixels belonging to a traffic light from a self-driving car's camera. We train a model with 99.88% performance. Pretty cool, right?

Actually, this model is useless ❌

Let me explain πŸ‘‡
The problem is the data is severely imbalanced - the ratio between traffic light pixels and background pixels is 800:1.

If we don't take any measures, our model will learn to classify each pixel as background giving us 99.88% accuracy. But it's useless!

What can we do? πŸ‘‡
Let me tell you about 3 ways of dealing with imbalanced data:

β–ͺ️ Choose the right evaluation metric
β–ͺ️ Undersampling your dataset
β–ͺ️ Oversampling your dataset
β–ͺ️ Adapting the loss

Let's dive in πŸ‘‡
Read 13 tweets

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