Show HN: How simple (but clever) algorithms can find label issues in datasets https://ift.tt/n6VgrBf

Show HN: How simple (but clever) algorithms can find label issues in datasets I built Vizzy as a hackathon project, with the goal of explaining how relatively simple (but clever) algorithms can be a powerful tool to automatically find issues in datasets, including label errors and out-of-distribution data. Vizzy uses a JavaScript port of (a part of) https://ift.tt/ZWpPveS , which implements the algorithms described in https://ift.tt/INPhcrt . There are other neat technical nuggets in the implementation of Vizzy as well, including ML model training in the browser (using features from a pretrained ResNet-18, performing truncated SVD, and using an SVM model for speed). If you’re interested in the details of how Vizzy works, check out this blog post: https://ift.tt/Y0RaEJ7 I’m happy to answer any questions related to Vizzy, cleanlab, or confident learning and data-centric AI in general! https://ift.tt/0LCsb1u September 8, 2022 at 05:23AM