DINO (computer vision)
DINO (self-DIstillation with NO labels) is a family of self-supervised learning methods for computer vision from Meta AI that trains Vision Transformers (ViTs) on unlabeled images and produces general-purpose…
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I-JEPA
I-JEPA (Image-based Joint-Embedding Predictive Architecture) is a self-supervised learning method for computer vision developed by Meta AI.
Joint Embedding Predictive Architecture
Joint Embedding Predictive Architecture (JEPA) is a family of self-supervised, non-generative neural network architectures proposed by Yann LeCun in his June 2022 position paper A Path Towards Autonomous…
Llama 2
Llama 2 is a family of open-weight large language models developed by Meta AI. Meta released pretrained and dialogue-tuned checkpoints with 7 billion, 13 billion, and 70 billion parameters on July 18, 2023.
data2vec
data2vec is a self-supervised learning framework from Meta AI (then Facebook AI Research) that applies the same training method to three different input types: speech, computer vision, and text.