“Recently took part in a new kind of ML competition based on Andrew Ng’s idea of shifting focus from model-centric to data-centric AI. Found cleanlab, a useful package in supporting this data-centric movement. It is based on the field of confident learning and helps to detect and learn in the presence of noisy real world labels.”

“I used an open-sourced library, cleanlab, to remove low-quality labels on an image dataset. The [ResNet] model trained on the dataset without low-quality data gained 4 percentage points of accuracy compared to the baseline model (trained on all data).”

“I’m just starting to get the hand of this and read on how it works. But right now from the first results it looks like pure black magic... So thank you for this!!”

“We’re looking into making Cleanlab a standard processing step whenever we get labels.”

“Improving your training data is more important that using the latest ‘state-of-the-art’ model. Here’s a very simple trick: use cleanlab, a Python package for machine learning with noisy labels and finding mislabeled data.”

“I’m just starting to get the hang of this and read on how it works. But right now from the first results it looks like pure black magic... So thank you for this!!”