Bias in ImageNet
Discover how biases infiltrate the ImageNet dataset through its collection process, which involved scraping images from Flickr and using crowd-sourced validation. Misclassifications arise from ambiguous classes, leading to surprising outcomes where models outperform human judgment in distinguishing between similar categories. This highlights the need for awareness and correction of biases in machine learning datasets.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)
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