Bias in Data Sets
Amir discusses the inherent biases present in data sets, particularly highlighting the dominance of dog classes in certain datasets. He emphasizes the need for advancements in self-supervised learning pipelines that minimize reliance on human labels, suggesting that true success will come when these methods can be applied to various image recognition challenges, such as depth estimation and object segmentation.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Trends in Computer Vision with Amir Zamir - #338
Related Questions
Is less labeled data needed for training machine learning models?
Is less labeled data needed for training machine learning models as discussed in the episodes Machine Learning on Images with Noisy Human-centric Labels and Unlocking Raw Data Sets?
Is less labeled data needed for training machine learning models according to the episode The Fallacy of "Ground Truth" with Shayan Mohanty - #576 and the clip Active Learning Insights?