Chapter
The Role of Data Augmentation in Machine Learning
58:25 - 1:05:22 (06:56)
This podcast discusses the importance and potential benefits of data augmentation in machine learning, particularly in the context of medical imaging. The speaker argues that incorporating data augmentation into the learning process can improve model performance and accuracy.
Clips
The speaker discusses data augmentation for vision, which involves image filtering operations like blurring, Instagram filters, and saturating images.
58:25 - 1:01:22 (02:56)
Summary
The speaker discusses data augmentation for vision, which involves image filtering operations like blurring, Instagram filters, and saturating images. They argue that data augmentation may involve more learning than the learning process itself.
ChapterThe Role of Data Augmentation in Machine Learning
Episode#206 – Ishan Misra: Self-Supervised Deep Learning in Computer Vision
PodcastLex Fridman Podcast
Data augmentation in the learning process can be useful especially in domains like medical imaging where geometric augmentation is not very valid for the human body.
1:01:22 - 1:05:22 (03:59)
Summary
Data augmentation in the learning process can be useful especially in domains like medical imaging where geometric augmentation is not very valid for the human body. It has been a key factor in creating robust neural networks for visual self-supervised learning.