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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

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A Stationary (And Therefore Compatible) Representation is All You Need.

Niccolo Biondi, Federico Pernici, Simone Ricci

IEEE Transactions on Pattern Analysis and Machine Intelligence

|May 11, 2026

Summary

This study shows stationary representations from d-Simplex fixed classifiers ensure model compatibility over time. Combining cross-entropy and contrastive loss captures higher-order dependencies for robust sequential model updates.

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Area of Science:

Machine Learning
Computer Vision
Artificial Intelligence

Background:

Learning compatible representations is crucial for models that are updated over time.
Sequential fine-tuning poses challenges for maintaining representation compatibility.
Existing methods may not capture higher-order dependencies between model updates.

Purpose of the Study:

To demonstrate that stationary representations from d-Simplex fixed classifiers imply compatibility.
To develop methods for learning compatible representations during sequential model fine-tuning.
To improve the robustness and performance of models undergoing updates and replacements.

Main Methods:

Utilizing d-Simplex fixed classifiers for learning stationary representations.
Employing a convex combination of cross-entropy and contrastive loss.
Conducting extensive experiments on sequential fine-tuning and model replacement scenarios.

Main Results:

Stationary representations learned by d-Simplex fixed classifiers satisfy the formal definition of compatibility.
The proposed loss combination captures higher-order dependencies, ensuring compatibility.
Achieved state-of-the-art performance in scenarios with sequential model updates and replacements.

Conclusions:

Stationary representations are foundational for compatible representations in machine learning.
The combined loss function effectively addresses challenges in sequential model updates.
Compatible representations enable uninterrupted services and improved performance during model evolution.