The stagenet dataset, collected for the last two months, was completed on July 1st and is ready to be aggregated and processed. This previous month consisted mostly of bug fixes and code optimizations, which resulted in the code-base being fully prepped to process the newly collected dataset. I also finished collecting a small mainnet validation dataset and used the pre-trained machine learning models to predict on it, assessing the real-world efficacy. While the preliminary testnet dataset achieved an accuracy of 34% in predicting the true spend of a ring signature, this did not translate to the mainnet dataset with an accuracy of only 11%. This flaw is detailed thoroughly in my technical analysis and ultimately is due to the highly flawed collection methods used for the preliminary dataset, which have been fixed in the newly collected dataset. In the next month, I will finish processing the new dataset, train it on the machine/deep learning models, and write a thorough analysis of the results.
If you would like to read the full technical update please refer to the following Github post: CLICK HERE!