CIIR - Class-Indexed Interface Representation Learning

Key points

• Multimodal learning commonly assumes paired or synchronized observations, yet independently collected datasets rarely provide such correspondence.1
• To demonstrate CIIR, we learn this address space from handwritten digits, clothing images, spoken-digit spectrograms, and hand signs without synchronized or instance-matched samples.1
• To demonstrate generalizability of CIIR, we reuse the ten addresses as code elements in collision-free arithmetic and hierarchical address codes for all 100 CIFAR-100 classes and evaluate direct arithmetic-code realization through the existing receivers.1
• These results establish CIIR as a compact interface for modular cross-dataset communication and post-hoc dataset attachment without exhaustive paired multimodal supervision.1

Sources (1)

• [1]CIIR - Class-Indexed Interface Representation Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 08:44 AM

  We introduce CIIR, a modular encoder--decoder framework that connects heterogeneous datasets through a class-indexed address space while preserving each dataset's native input format and output module.

  Multimodal learning commonly assumes paired or synchronized observations, yet independently collected datasets rarely provide such correspondence.

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