AI Draws New Pokemon using Simple Math

I feel like this is just two pokemon pics layed on top of each other with one having very low opacity.

Great job! I've got some advice for improving this: - Increase your dataset by more than 100x with small random translations and horizontal flips. Do it each batch or epoch if you don't have the memory to pre-compute it. - Once you start introducing more data, don't use the 4000+ dimensional vector, use a regular autoencoder. - Blending should be done by linear interpolation of the feature vector, the bottle-neck of the autoencoder. - Use a bigger model but add dropout to compensate for that. You should get drastically improved results. Maybe I'll give it a try real quick to see ;)

2:32 did u really just use google to search for google..

"Aloha versions .. whatever that means." Ahahahahah

Eyy Jabrils, love the video and... Please don't let us wait another month for the next episode 😅

I'd honestly try with more variation. First of all, having reflections in there seems like a good idea - though have reflection for all of them Second, what you could do is take out the background color (make it transparent) and replace it either by a random (at-training-time!) flat color or even by random noise, artificially inflating the training set by a HUGE factor while keeping the relevant features consistent. You could even use that to make it do transparent sprites: Anything that's background ought to be put in a transparency channel. Third you could use the multiple facial expressions, but make sure to have every Pokémon have an equal chance of coming up, basically by first sampling from Pokémon number (or something like that), and then from expression (if applicable for the Pokémon)

This man said aloha versions.. 🤣🤣🤣

These pokemon loom like half opacity overlaps w hella noise lmao

My dude, I think you've been hax.

The fact that at the Horsea+Charizard combo it made a Horsea with angry eyes is pretty dope.

It looks like those images are just overlapped with transparency. Is that normal?

There is a website where you can get Pokémon fusions so you can have a potential data set of MUCH more

We are seeing sqrt(2). Or more specifically the vector is only sqrt(2)/2 ≈ 0.7 long

I may be wrong, but isn't flipping the image one of the recommended ways to expand a dataset?

Flipping images is a great way to increase your dataset size. Andrew Ng discusses this in his machine learning course. However, exact duplicates are bad. Make sure to remove those. Thanks for a great video. I love your content 😁

To answer that question at the end: It's like a 50% transparency of the two. But here's why I think that's important, and why I think you're on to something: It looks like the neural network has, perhaps, memorized a compressed version of each pokémon, instead of memorizing a collection of features in an arrangement that make up a pokémon. But how to fix this?… An alteration to the architecture? An alteration to the dataset? Both? How to do either?… What about adding more layers to the neutral network, but make the layers fairly small? But make a relatively smooth gradient of change to the number of nodes per layer. I'm thinking a neural network somewhere in the ballpark of this: [1st layer node number = (width/2)*(height/2)],[2nd layer node number = (width of 1st node layer/2)*(height of 1st node layer/2)],[3rd layer node number = (width of 2nd node layer/4)*(height of 2nd node layer/4)],[4th layer node number = (width of 3rd node layer/2)*(height of 3rd node layer/2)],[same number of nodes as previous layer],[same number of nodes as previous layer],[same number of nodes as previous layer] Something like that. Wouldn't it be nice if I could communicate this idea a bit more elegantly? sigh The idea is to choose an architecture that makes it very difficult for memorization, but easy for abstraction. Hopefully while maintaining a visually appealing result… But no promises!

3:51 Jabrils: “there was only about 150” PNG 151-251: “am I a joke to you ?”

"Aloha" pokemon forms? Are you trying to trigger pokemon fans? IT'S ALOLA FORMS

Data Cleanup is 90% of any machine learning project.

Really enjoy the videos you make. Great style and fun projects. Thanks for the content and here is to many more years of fun!