Originally published on Towards AI. I used an open-sourced library, cleanlab, to remove low-quality labels on an image dataset. The model trained on the dataset without low-quality data gained 4 percentage points of accuracy compared to the baseline model (trained on all data). Improving data quality sounds easy enough. But the workload of manually checking data quality can quickly become insurmountable as the dataset scales.

In a new format, DeepLearning.AI and Landing.AI collaborated to announce -- the Data-Centric AI Competition whereby participants are asked to improve a dataset given a fixed model. Andrew Ng invited participants to be a part of this unique competition. Starting from June 17, contestants can submit their altered dataset for evaluation latest by September 4 2021 – the birth date of John McCarthy, who coined the term artificial intelligence – by 6 PM PT (06:30 AM IST). The top three winners from each of the two categories (Best Performance Overall and Most Innovative) will be invited to a private event with Andrew Ng to share ideas about how to grow the data-centric movement. They will be highlighted in The Batch and other DeepLearning.AI and Landing AI channels.