aiqbench analysis
This arXiv paper is primarily about machine learning: it proposes topological regularization so that two autoencoders trained independently produce latent spaces that can be decoded interchangeably, without coordination during training. The theoretical result is conditional, and the evidence is reconstruction experiments on several datasets, so the claim of "approximate geometric compatibility" should be read as a promising method rather than a general guarantee. For ML practitioners, the practical question is whether this kind of latent alignment can hold up across architectures, scales, and pre-trained models.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations
arXiv:2610.19401v1 Announce Type: new Abstract: 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. 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. 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. 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. Reconstruction experiments validate this approach empirically, substantially narrowing the interoperability gap across multiple datasets.
Abstract: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. 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. 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. 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. Reconstruction experiments validate this approach empirically, substantially narrowing the interoperability gap across multiple datasets.
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
arXiv:2610.19401 [cs.LG]
(or arXiv:2610.19401v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.19401 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 241 words
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