Chapter
Data Augmentation for Neural Networks
52:10 - 58:25 (06:14)
Data augmentation is the practice of artificially increasing the size of a training set by distorting images without changing the nature of the image, which can improve the performance of neural networks in image recognition tasks.
Clips
The concept of data augmentation involves increasing the size of a training set while intentionally distorting the images in a way that doesn't change the image's nature.
52:10 - 53:39 (01:29)
Summary
The concept of data augmentation involves increasing the size of a training set while intentionally distorting the images in a way that doesn't change the image's nature. This technique has evolved in recent years and has become vital in self-supervised learning techniques to pre-train vision systems.
ChapterData Augmentation for Neural Networks
Episode#258 – Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning
PodcastLex Fridman Podcast
Siamese networks use identical copies of neural nets to produce an output representation that ensures semantically similar items have similar representations and different items have different representations.
53:39 - 58:25 (04:45)
Summary
Siamese networks use identical copies of neural nets to produce an output representation that ensures semantically similar items have similar representations and different items have different representations. The networks can be used for data augmentation or to compare different views of the same scene.