[Paper] SerpentFlow: Generative Unpaired Domain Alignment via Shared-Structure Decomposition

Source: arXiv - 2601.01979v1

Overview

SerpentFlow tackles the classic problem of domain alignment when you have no paired data—think of trying to translate between two image styles or resolutions without any exact before‑and‑after examples. The authors propose a generative framework that first splits each sample into a shared structural component and a domain‑specific residual, then uses this split to synthesize pseudo‑pairs that let conditional generative models be trained as if paired data existed. The result is a powerful, data‑driven way to upscale or downscale data across domains while preserving the underlying low‑frequency “shape” and realistically filling in high‑frequency details.

Key Contributions

• Shared‑Structure Decomposition (SSD): A novel latent‑space factorisation that isolates domain‑agnostic structure from domain‑specific noise.
• Pseudo‑pair Generation: By swapping the domain‑specific part with stochastic noise, the method creates synthetic training pairs for conditional generation in an otherwise unpaired setting.
• Automatic Frequency Cutoff: A classifier‑based criterion automatically determines the low‑ vs. high‑frequency split, adapting to each dataset without manual tuning.
• Flow‑Matching Integration: Implements the generative step with flow‑matching, showing compatibility with other conditional generators (e.g., diffusion, GANs).
• Broad Empirical Validation: Demonstrates the approach on synthetic images, physics simulations, and a real‑world climate downscaling task, achieving high‑fidelity reconstructions of fine‑scale details.

Methodology

1. Encode into Latent Space – Both source and target domain samples are passed through a shared encoder that produces a latent representation.
2. Decompose Latent Vector
   • Shared Component (S): Captures low‑frequency, domain‑invariant structure (e.g., overall shape, coarse temperature fields).
   • Domain‑Specific Component (D): Holds high‑frequency, domain‑dependent details (textures, turbulence, fine‑scale weather patterns).
3. Learn the Cutoff Frequency – A lightweight classifier evaluates how well a candidate frequency split separates structure from detail; the split that maximises classification confidence is selected automatically.
4. Create Pseudo‑Pairs
   • Keep S from a source sample.
   • Replace D with random noise drawn from a learned prior.
   • Decode the combined latent vector into a synthetic target‑domain sample.
5. Conditional Generation – Train a conditional generative model (here, a flow‑matching network) to map S → target sample, using the pseudo‑pairs as supervision.
6. Inference – At test time, encode a low‑resolution (or otherwise coarse) input, extract S, and let the trained generator synthesize the high‑resolution output, automatically injecting realistic high‑frequency details.

Results & Findings

Key takeaways

• The shared component reliably captures the low‑frequency “ground truth” across domains, enabling the generator to focus on realistic high‑frequency synthesis.
• Automatic frequency selection removes a major hyper‑parameter headache common in multi‑scale methods.
• Flow‑matching provides stable training and fast sampling compared to diffusion‑based alternatives.

Practical Implications

• Image & Video Upscaling: Developers can plug SerpentFlow into pipelines that need high‑quality upscaling without a curated paired dataset (e.g., legacy game assets, medical imaging).
• Scientific Simulations: Researchers can accelerate expensive high‑resolution simulations by training a model on cheap coarse runs and then “hallucinating” fine details on demand.
• Climate & Weather Modeling: Operational forecasters can generate high‑resolution regional forecasts from global models, reducing computational load while preserving local extremes.
• Cross‑Domain Transfer: Any scenario where two modalities share a common low‑frequency backbone (audio‑spectrogram ↔ visual waveform, text summarization ↔ full article) can benefit from the pseudo‑pair trick, turning unpaired data into a supervised training signal.
• Modular Integration: Because SSD is agnostic to the downstream generator, teams can keep their existing conditional GAN or diffusion setups and simply add the decomposition layer.

Limitations & Future Work

• Assumption of Shared Low‑Frequency Structure: The method hinges on the existence of a meaningful common backbone; domains that differ fundamentally (e.g., photos vs. sketches with no geometric overlap) may break the decomposition.
• Latent Space Quality: The encoder must be expressive enough to separate structure from detail; sub‑optimal encoders can leak domain‑specific cues into the shared component, hurting generation quality.
• Scalability of Frequency Classifier: While lightweight, the classifier adds an extra training step; scaling to ultra‑high‑resolution data may require more efficient frequency‑selection heuristics.
• Generative Model Choice: The paper demonstrates flow‑matching, but performance can vary with other generators; systematic benchmarking across GANs, diffusion, and normalizing flows remains open.
• Future Directions: Extending SSD to multi‑modal settings, exploring hierarchical decompositions (multiple frequency bands), and integrating uncertainty quantification for safety‑critical applications like climate forecasting.

Authors

• Julie Keisler
• Anastase Alexandre Charantonis
• Yannig Goude
• Boutheina Oueslati
• Claire Monteleoni

Paper Information

• arXiv ID: 2601.01979v1
• Categories: cs.LG, cs.NE
• Published: January 5, 2026
• PDF: Download PDF