Self Supervised Learning (SSL)

Countless intriguing developments have been afoot in AI and Machine Learning (ML) over recent weeks. If you've been keeping an ear to the ground, you've likely heard whispers of an enigmatic concept called Self-Supervised Learning (SSL), forming the prime focus of our engaging Substack discussion today.

SSL, a subset of Unsupervised Learning, is gaining traction in the AI/ML community. It’s secret? Synthetic labels. These artificial tags, essentially 'corruptions' of the data, can be utilized to train Neural Networks (NNs), allowing them to develop robust representations of presented data and encouraging swift and efficient learning of novel tasks, even from a single presentation (Zero-shot).

Do we have your attention?

Let’s hack away deeper into this complex world with our latest Substack article, delving into diverse relevant domains:

1. Natural Language Processing (NLP): We discuss how SSL can be incorporated in training a language model using methods like MLM (Masked Language Model) and LM (Language Model).

2. Speech: Take a deep dive into Wav2Vec 2.0, a model utilizing SSL for voice recognition and synthesis.

3. Vision: Introducing "VicReg", a unique SSL implementation, and AutoEncoders, which use SSL to generate insightful images.

4. VAE: Variational AutoEncoders further illustrate SSL's potential. We explore their fascinating framework, including how they adopt diffusing models, a class of Markovian Hierarchical VAE.

Our Substack article concludes that SSL helps models learn powerful data representations in a cost-effective manner. These representations are instrumental in transfer learning and fine-tuning, with potential for zero-shot tasks like those explored in GPT-3!

Read more about it here: https://dubverseblack.substack.com/p/self-supervised-learnin...