Finding Rare Examples
Active learning is crucial for efficiently utilizing large datasets, especially when seeking rare examples. The conversation highlights the importance of data augmentation techniques to enhance the utility of these examples. In the context of autonomous driving, the challenge shifts from naming every object to effectively categorizing broad groups, emphasizing the need for innovative approaches in machine learning.In this clip
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