I-JEPA from Meta AI - A Human-Like Computer Vision Model | Paper Summary скачать в хорошем качестве

I-JEPA from Meta AI - A Human-Like Computer Vision Model | Paper Summary

In this video, we summarize a new groundbreaking research paper from Meta AI - Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture. As part of this paper they present I-JEPA, shortcut for Image-based Joint-Embedding Predictive Architecture, a new open-source computer vision model, which is the first model that follows Yann LeCun's vision for a more human-like AI, which he presented last year in his paper - A Path Towards Autonomous Machine Intelligence. There is no need for prior knowledge of this paper in order to understand this video. I-JEPA presents a new approach for self-supervised learning from images, so we first provide background about what are the current common self-supervised methods in computer vision, and then present the difference from the new approach presented in the paper. We also touch on why the new model is more human-like comparing to previous models. Additionally, we dive deep into the details of how I-JEPA training process works, which allows training an encoder that can generated highly semantic representation for input images. Blog post - https://aipapersacademy.com/i-jepa-a-... Paper on arxiv - https://arxiv.org/abs/2301.08243 👍 Please like & subscribe if you enjoy this content ---------------------------------------------------------------------------------- Support us - https://paypal.me/aipapersacademy ---------------------------------------------------------------------------------- Chapters: 0:00 Introducing I-JEPA 0:45 Self-Supervised Learning For Images 2:55 I-JEPA SSL Approach 4:42 I-JEPA Deep Dive