Building Multimodal Search and RAG

Created: by Pradeep Gowda Updated: May 22, 2024 Tagged: course · deep-learning

Course notes from Building Multimodal Search and RAG about Multimodal Learning.

  • Instructor: Sebastian Witalec, head of developer relations at Weaviate,
  • human’s learn multi-modally
  • human’s foundational knowledge is built using multi-modal interaction with the world and not via language.
  • multi-modal embedding models produce a joint embedding space that understand all your modalities (e.g.: text, video, audio, picture).
  • Training:
    • start with specialist models
    • unify these models
    • the vectors that they generate are going to be similar
    • Models are unified using a process called contrastive representative learning.
      • CRL is a general purpose process that can be used to train any embedding model.
      • create one unified vector space representation for multiple modalities
        • by giving positive and negative examples, we train the models to pull closer vectors together for similar examples. and push away dissimilar examples
        • Anchor point
        • The pushing pulling process is achieved with the contrastive loss function.
          • encode the anchor and the (+ve and -ve ) examples into vector embeddings.
          • calculate the distance between the anchor and the examples.
          • minimize the distance between the positive example and maximize the negative example during the training process.
          • in multimodal learning, the examples can be from different modalities.
          • Minimize the loss… Girdhar, Rohit, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. “Imagebind: One embedding space to bind them all,” 2023.
               
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