Chapter
Understanding Data Augmentation for Neural Networks
53:07 - 58:25 (05:17)
This podcast discusses the concept of data augmentation, which involves perturbing and augmenting data to improve a neural network's performance. It explores the idea of incorporating wild and physically consistent data, and the importance of feature vectors.
Clips
Data augmentation is a technique used in machine learning to increase the size of the dataset by perturbing or augmenting the existing data to create variations that can improve the performance of models.
53:07 - 56:45 (03:37)
Summary
Data augmentation is a technique used in machine learning to increase the size of the dataset by perturbing or augmenting the existing data to create variations that can improve the performance of models. By using this technique, the neural network can output a vector of features that represent different crops of the image with different lighting, contrast or colors.
ChapterUnderstanding Data Augmentation for Neural Networks
Episode#206 – Ishan Misra: Self-Supervised Deep Learning in Computer Vision
PodcastLex Fridman Podcast
In this episode, the speaker talks about exploring the use of imagination in neural networks, particularly in masking out parts of an image and imagining physcially consistent objects.
56:45 - 58:25 (01:39)
Summary
In this episode, the speaker talks about exploring the use of imagination in neural networks, particularly in masking out parts of an image and imagining physcially consistent objects.