Train to Stitch: Topology-Preserving Regularization for Interoperable Representations

Key points

• As large, powerful pre-trained models become more prevalent, there is growing interest in developing methods that allow different neural networks to be seamlessly combined with little or no direct communication between them.1
• Take autoencoder-based data compression as an example: the ability to independently choose architectures and train encoder and decoder models, while still ensuring they work together, could fundamentally transform how such models are built, deployed, and updated.1
• We provide a conditional theoretical analysis showing how the structural agreement encouraged by our regularizer can translate into approximate geometric compatibility between independently learned representations.1
• Reconstruction experiments validate this approach empirically, substantially narrowing the interoperability gap across multiple datasets.1

Sources (1)

• [1]Train to Stitch: Topology-Preserving Regularization for Interoperable Representations
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 01:49 AM

  In this work, we introduce a novel approach that leverages topological regularization to align the latent spaces of two independently trained autoencoder models, entirely eliminating the need for coordination during training.

  As large, powerful pre-trained models become more prevalent, there is growing interest in developing methods that allow different neural networks to be seamlessly combined with little or no direct communication between them.

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