Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output.
On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte).
It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my AMD 7800XT).
Checkout the repo - https://github.com/samyak112/pym-particles
I am curious. A classic machine learning ensemble approach is to overfit a collection of small models then bag them (e.g. voting) allowing the models to generalize.
I'm sure someone's tried to overfit a bunch of transformers for compression like this, then bag them to see how well it does?
So the codec would be something like: <header describing image size + transformer layer shape> <transformer data itself>
I've seen experiments where people have a "fixed" pipeline but I think having something more dynamic would work quite well.
1. How much was AI used to generate documentation for this project?
2. The 100MB CSV data sources are not provided in the repo so it doesn't seem possible to reproduce your results. The enwik9 dataset says it is a "slice" of the larger data set, and there are many NYC taxi trip record datasets that exist. Can you provide the datasets used to generate your results?
3. I am surprised to see performance comparisons only between your transformer and WinZIP. What were your results when comparing your transformer to more modern approaches like LZMA2 (level 9), BZIP2 and ZPAQ (max effort)?
I tested with 100 MB files because anything larger takes a long time to evaluate. The actual target was at least 1 GB, and in that case I would use a 100 MB model (Shannon entropy rules).
I also tried it on a 100 MB Photoshop file and was able to compress it down to 45 MB, whereas ZIP could only get it down to 60 MB. So yeah still not losing gains.
I know the top submission was able to get it to 13 mb.
Still trying some ideas to get better compression.
Edit: oh wait that's too easy. Need to generate /publish random digits so everyone can use it.
Random data does not mean it does not match a pattern in your dictionary for example.
[0]: https://en.wikipedia.org/wiki/Randomness
[1]: https://en.wikipedia.org/wiki/Data_compression
But it’s only for the game I’m building and it’s not pure compression work, I had to do some tricky things
For context these numbers are for a grid based game where players can perform 4 actions per second, and the numbers I’m sharing are for 30 minutes of gameplay with anywhere from 2-1024+ players (human players) playing simultaneously
So if you do the math, my compression feat is effectively ~99% compression on naive best case. And if you compare it to the raw data, it’s closing in on an even higher number than that I haven’t done the math but the raw data is another factor of 10 greater than ~100KB so the “compression” versus raw data is ~99.9%
It sounds absolutely bullshit I know :D
But I will be posting a blog post soon once I release the game.
I do compression in quotes because it’s not a pure compression feat, the 99%+ feat is effectively being clever about what actually requires compression to achieve the same outcome
I started looking into diffing the state, compression, etc... until I realized, wait a minute! My player movement is linear so I only need a packet for start and stop! And so I achieved near infinite efficiency improvement :)
I think the word is... a specialized solution can beat a general one.
Also, "remembering what the program actually needs to do, and just making it do that"... I de-pessimized the netcode: https://youtube.com/watch?v=pgoetgxecw8
Clever insight :) yes a specialised solution usually wins! Good effort
Did you end up publishing your game?