The Role of Data Augmentation in Self-Supervised Learning

Ishan Misra is a research scientist at FAIR working on self-supervised visual learning. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(07:49) – Self-supervised learning
(16:24) – Self-supervised learning is the dark matter of intelligence
(20:17) – Categorization
(28:50) – Is computer vision still really hard?
(32:35) – Understanding Language
(42:14) – Harder to solve: vision or language
(48:59) – Contrastive learning & energy-based models
(52:59) – Data augmentation
(57:19) – Fixed audio spike by lowering sound with pen tool
(1:05:33) – Real data vs. augmented data
(1:09:16) – Non-contrastive learning energy based self supervised learning methods
(1:12:54) – Unsupervised learning (SwAV)
(1:15:37) – Self-supervised Pretraining (SEER)
(1:20:44) – Self-supervised learning (SSL) architectures
(1:26:43) – VISSL pytorch-based SSL library
(1:29:38) – Multi-modal
(1:37:06) – Active learning
(1:42:45) – Autonomous driving
(1:54:12) – Limits of deep learning
(1:58:19) – Difference between learning and reasoning
(2:03:26) – Building super-human AI
(2:11:14) – Most beautiful idea in self-supervised learning
(2:15:02) – Simulation for training AI
(2:18:27) – Video games replacing reality
(2:19:40) – How to write a good research paper
(2:24:08) – Best programming language for beginners
(2:25:01) – PyTorch vs TensorFlow
(2:28:26) – Advice for getting into machine learning
(2:30:31) – Advice for young people
(2:32:58) – Meaning of life