Representation Selection via Cross-Model Agreement using Canonical Correlation Analysis

• URL: http://arxiv.org/abs/2604.00921v1
• Date: Wed, 01 Apr 2026 14:01:41 GMT
• Title: Representation Selection via Cross-Model Agreement using Canonical Correlation Analysis
• Abstract summary: We propose a simple, training-free method to improve the efficiency of image representations via a post-hoc canonical correlation analysis operator.<n>By leveraging the shared structure between representations produced by two pre-trained image encoders, our method finds linear projections that serve as a principled form of representation selection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern vision pipelines increasingly rely on pretrained image encoders whose representations are reused across tasks and models, yet these representations are often overcomplete and model-specific. We propose a simple, training-free method to improve the efficiency of image representations via a post-hoc canonical correlation analysis (CCA) operator. By leveraging the shared structure between representations produced by two pre-trained image encoders, our method finds linear projections that serve as a principled form of representation selection and dimensionality reduction, retaining shared semantic content while discarding redundant dimensions. Unlike standard dimensionality reduction techniques such as PCA, which operate on a single embedding space, our approach leverages cross-model agreement to guide representation distillation and refinement. The technique allows representations to be reduced by more than 75% in dimensionality with improved downstream performance, or enhanced at fixed dimensionality via post-hoc representation transfer from larger or fine-tuned models. Empirical results on ImageNet-1k, CIFAR-100, MNIST, and additional benchmarks show consistent improvements over both baseline and PCA-projected representations, with accuracy gains of up to 12.6%.

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