Machine Learning Street Talk (MLST) cover image

SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Mathilde Caron)

Machine Learning Street Talk (MLST)

00:00

Balancing Data and Induction in Machine Learning

This chapter examines the interplay between data quantity and the development of inductive priors in enhancing machine learning models. It highlights the significance of both extensive datasets and sound foundational methodologies through various examples, including feature visualization in neural networks. The discussion further explores unsupervised learning, challenges in image augmentation, and the iterative nature of research, emphasizing the journey of learning over mere results.

Transcript
Play full episode

The AI-powered Podcast Player

Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
App store bannerPlay store banner
Get the app