# Unified Representation Learning for Cross Model Compatibility

arXiv: https://arxiv.org/abs/2008.04821 · HTML: https://mlsanity.com/paper/2008.04821/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 30 of 30)

- [Learning Compatible Embeddings](https://mlsanity.com/paper/2108.01958/) (2021)
- [Compatibility-aware Heterogeneous Visual Search](https://mlsanity.com/paper/2105.06047/) (2021)
- [Towards Universal Backward-Compatible Representation Learning](https://mlsanity.com/paper/2203.01583/) (2022)
- [Forward Compatible Training for Large-Scale Embedding Retrieval Systems](https://mlsanity.com/paper/2112.02805/) (2021)
- [Continual Learning for Visual Search with Backward Consistent Feature Embedding](https://mlsanity.com/paper/2205.13384/) (2022)
- [FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition](https://mlsanity.com/paper/2112.12496/) (2021)
- [CoReS: Compatible Representations via Stationarity](https://mlsanity.com/paper/2111.07632/) (2021)
- [Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning](https://mlsanity.com/paper/2108.02959/) (2021)
- [Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval](https://mlsanity.com/paper/2204.13919/) (2022)
- [BT2: Backward-compatible Training with Basis Transformation](https://mlsanity.com/paper/2211.03989/) (2022)
- [boldsymbolλ-Orthogonality Regularization for Compatible Representation Learning](https://mlsanity.com/paper/2509.16664/) (2025)
- [Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval](https://mlsanity.com/paper/2201.09724/) (2022)
- [FastFill: Efficient Compatible Model Update](https://mlsanity.com/paper/2303.04766/) (2023)
- [CL2R: Compatible Lifelong Learning Representations](https://mlsanity.com/paper/2211.09032/) (2022)
- [Improving Prediction Backward-Compatiblility in NLP Model Upgrade with Gated Fusion](https://mlsanity.com/paper/2302.02080/) (2023)
- [Binary Embedding-based Retrieval at Tencent](https://mlsanity.com/paper/2302.08714/) (2023)
- [Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems](https://mlsanity.com/paper/2103.11766/) (2021)
- [Darwinian Model Upgrades: Model Evolving with Selective Compatibility](https://mlsanity.com/paper/2210.06954/) (2022)
- [Towards Cross-modal Backward-compatible Representation Learning for Vision-Language Models](https://mlsanity.com/paper/2405.14715/) (2024)
- [Large-to-small Image Resolution Asymmetry in Deep Metric Learning](https://mlsanity.com/paper/2210.05463/) (2022)
- [Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning](https://mlsanity.com/paper/2506.05826/) (2025)
- [Dynamic Gradient Reactivation for Backward Compatible Person Re-identification](https://mlsanity.com/paper/2207.05658/) (2022)
- [Backward-Compatible Aligned Representations via an Orthogonal Transformation Layer](https://mlsanity.com/paper/2408.08793/) (2024)
- [Asymmetric Image Retrieval with Cross Model Compatible Ensembles](https://mlsanity.com/paper/2303.17531/) (2023)
- [MixBCT: Towards Self-Adapting Backward-Compatible Training](https://mlsanity.com/paper/2308.06948/) (2023)
- [Towards Test-time Efficient Visual Place Recognition via Asymmetric Query Processing](https://mlsanity.com/paper/2512.13055/) (2025)
- [A Stationary (and Therefore Compatible) Representation is All You Need](https://mlsanity.com/paper/2606.12488/) (2026)
- [Learning Unified Representations for Multi-Resolution Face Recognition](https://mlsanity.com/paper/2310.09563/) (2023)
- [Bi-C2R: Bidirectional Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification](https://mlsanity.com/paper/2512.25000/) (2025)
- [Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility](https://mlsanity.com/paper/2609.16875/) (2026)

## Abstract (arXiv)

We propose a unified representation learning framework to address the Cross Model Compatibility (CMC) problem in the context of visual search applications. Cross compatibility between different embedding models enables the visual search systems to correctly recognize and retrieve identities without re-encoding user images, which are usually not available due to privacy concerns. While there are existing approaches to address CMC in face identification, they fail to work in a more challenging setting where the distributions of embedding models shift drastically. The proposed solution improves CMC performance by introducing a light-weight Residual Bottleneck Transformation (RBT) module and a new training scheme to optimize the embedding spaces. Extensive experiments demonstrate that our proposed solution outperforms previous approaches by a large margin for various challenging visual search scenarios of face recognition and person re-identification.