JEPA Architectures - How neural networks learn abstract concepts about images (IJEPA) скачать в хорошем качестве

JEPA Architectures - How neural networks learn abstract concepts about images (IJEPA)

This video explains Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture paper that proposes a revolutionary approach for "human-like" Machine Learning training. The video dives into the ideologies behind JEPA methods, network architectures, results, and comparison with existing generative methods (like Masked Autoencoders) and contrastive learning methods (like SimCLR). Follow on Twitter: @neural_avb To support me, consider JOINING the channel. Members get access to Code, project files, scripts, slides, animations, and illustrations for most of the videos on my channel! Learn more about perks below. Join and support the channel - https://www.youtube.com/@avb_fj/join Learn more about contrastive learning in my breakdown video about Multimodal ML:    • Multimodal AI from First Principles -...   Papers referenced: I-JEPA: https://arxiv.org/pdf/2301.08243.pdf Yan Lecun's original human-like AI paper: https://openreview.net/pdf?id=BZ5a1r-... SimCLR: https://arxiv.org/pdf/2002.05709.pdf Masked AE: https://arxiv.org/pdf/2111.06377.pdf RCDM: https://arxiv.org/pdf/2112.09164.pdf Timestamps: 0:00 - Intro 1:05 - Why IJEPA? 5:22 - Network architecture 7:43 - Results 8:50 - Summary #deeplearning #computervision #ai #machinelearning