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thank you yeah this project was a lot of fun uh a lot of my research at school has mainly been math so itamp;#39;s it was a I guess a refreshing change of pace so right so Iamp;#39;m sure a lot of people here know um that a lot of the success of modern machine learning is built on the large volumes of labeled training data and so the conventional wisdom here is that the more data the better right uh this allows us to generalize a better to unseen or unseen samples um but you know obtaining labels for data doesnamp;#39;t come for free uh thereamp;#39;s still a large bottleneck in in sort of our model development pipelines um and today weamp;#39;ll be focusing on the image domain where uh sort of weamp;#39;ve have increasingly complex labels so um uh the image here is an example from a recent paper where they obtain pixel level labels for human faces uh 34 uh some what obscure classes uh of the of the face Iamp;#39;m not sure whether Scalera is um but they label it and and so you
