<p>Today I would like to share my thoughts on self-supervised learning (SSL) - one of the key ingredients behind recent breakthroughs in artificial intelligence.</p>
<p>SSL allows models to learn from vast amounts of unlabeled data, which has expanded the capabilities of deep learning in many areas. Today, SSL underpins state-of-the-art models in natural language processing (e.g., translation and large language models), audio (e.g., data2vec), and computer vision (e.g., the SEER model trained on a billion images and DINOv2).</p>
<p>However, training SSL models is akin to preparing a gourmet dish - it’s a complex art with a high barrier to entry. A successful SSL “recipe” requires the right approach to task selection and carefully curated hyperparameters.</p>
<p>Recently, a new “Self-Supervised Learning Cookbook” was published, which serves as a practical guide for researchers and AI practitioners to master SSL and experiment with its capabilities. The book also contains tips and tricks from over a dozen authors from various universities and renowned researchers from Meta AI, such as Yann LeCun.</p>
<p>Thanks to this guide, researchers will be able to master the fundamental techniques and principles of SSL, as well as gain access to recommendations for selecting hyperparameters, architectures, and optimizers. This will help them successfully implement SSL methods and continue to push this field forward.</p>
<p>Paper: <a href="https://arxiv.org/abs/2304.12210">https://arxiv.org/abs/2304.12210</a></p>;
#ai #ssl #gpt #llm