Cross-Dataset Generalization

• Cross-dataset generalization is the ability of a model to maintain predictive performance when applied to new datasets with differing acquisition protocols, annotation styles, and content distributions.
• It employs rigorous evaluation protocols, such as leave-one-dataset-out and performance matrices, using metrics like AUC, F₁, and Dice scores to quantify the generalization gap.
• Practical strategies including ensemble learning, diverse pretraining, and data augmentation are used to mitigate overfitting and enhance robustness across varied domains.

Cross-dataset generalization refers to the ability of a model, algorithm, or system trained on data from one or several domains (“source datasets”) to retain strong predictive performance when exposed to new, unseen domains (“target datasets”) that differ in acquisition protocols, annotation style, content distribution, or domain-specific artifacts. This property is critical for reliable deployment and benchmarking in real-world settings, where distribution shift is inevitable. The paper and assessment of cross-dataset generalization has emerged as a central concern across diverse fields—vision, language, biomedical prediction, security, autonomous systems—each demonstrating both the technical challenges and methodological approaches to robust generalization.

1. Definitions, Formalism, and Metrics

Fundamental to cross-dataset generalization is the distinction between in-domain performance (train/test splits from the same dataset) and out-of-domain performance (train on one dataset, test on another). The generalization gap quantifies the performance drop: $\Delta_M = M_{\mathrm{in}} - M_{\mathrm{out}}$ where $M$ is any relevant metric: AUC, F₁, Dice score, MCC, RMSE, classification accuracy, etc. Small $\Delta_M$ implies strong generalization; large values indicate overfitting to source-specific features (Gesnouin et al., 2022, Cantone et al., 15 Feb 2024, Partin et al., 18 Mar 2025).

In segmentation and regression, mean Dice, area under the precision-recall curve (AUPR), and absolute errors are commonly reported (Playout et al., 14 May 2024, Zhang et al., 15 Oct 2024, Vance et al., 2023). For classification tasks, receiver operating characteristic (ROC) and calibration curves, macro-F₁, and confusion-matrix-based metrics are standard (Gesnouin et al., 2022, Nejadgholi et al., 2020).

Multidataset evaluations often rely on a performance matrix $G$ with entries $g[s, t]$ representing model performance trained on source $s$ and tested on target $t$. Normalized cross-dataset metrics and drop ratios further characterize robustness (Partin et al., 18 Mar 2025).

2. Sources of Distribution Shift and Dataset Analysis

Cross-dataset generalization is undermined by diverging:

• Input distributions: Variations in sensor resolution, scene appearance, color balance, class imbalance, and spatial layout produce shifts not captured in source datasets (Gesnouin et al., 2022, Playout et al., 14 May 2024, Grimm et al., 24 Jul 2025, Lichy et al., 24 Jan 2024).
• Annotation protocols/guidelines: Differing granularity (fine vs. coarse), label definitions, and reader consensus lead to source-target label alignment issues (Playout et al., 14 May 2024, Jalocha et al., 18 Jul 2025, Nejadgholi et al., 2020).
• Data artifacts/taxonomies: Topic biases, annotation artifacts, and per-domain vocabulary or conventions can create spurious correlations learned by models (e.g., label-conditional keyword patterns in NLI and online abuse detection) (Zhang et al., 2019, Nejadgholi et al., 2020, Jalocha et al., 18 Jul 2025).
• Structural or morphological coverage: Limited diversity (e.g., in spatial area coverage, grayscale levels, lesion sizes, signature styles) restricts the learned mapping to a subregion of the target domain (Zhang et al., 15 Oct 2024, Playout et al., 14 May 2024).

Rigorous dataset characterization and clustering by annotation style are essential prior to merging or evaluating generalization (Playout et al., 14 May 2024). Tools such as LDA topic modeling and distributional analysis can expose and mitigate non-generalizable biases (Nejadgholi et al., 2020).

3. Evaluation Protocols and Empirical Results

Standardized cross-dataset evaluation protocols entail:

• Training on one or several source datasets.
• Testing, without retraining, on disjoint target datasets.
• Aggregating results across multiple splits and comparing with within-dataset benchmarks (leave-one-dataset-out, zero-shot, or multi-source settings) (Gesnouin et al., 2022, Partin et al., 18 Mar 2025, Playout et al., 14 May 2024).

Empirical findings consistently reveal substantial degradation: example drops of AUC (0.75→0.60), F₁ (0.65→0.45) in pedestrian crossing prediction (Gesnouin et al., 2022); mean Dice drops of 3–5% (segmentation) (Playout et al., 14 May 2024); MCC and F₁ dropping to near chance in network intrusion (Cantone et al., 15 Feb 2024); AUPR and EER deteriorating in signature verification (Parracho, 20 Oct 2025); and drug response R² reductions of 0.2–0.3 (Partin et al., 18 Mar 2025).

Performance drop matrices and normalized generalization scores (performance ratio to in-domain) offer quantitative benchmarks. In some cases, domain mixing or synthetic-to-real transfer can even slightly improve generalization, but the typical outcome is marked loss of reliability.

4. Methodological Advances to Improve Generalization

Several strategies have been empirically validated to enhance cross-dataset generalization:

• Ensembles: Averaging predictions from models trained on different sites or with varied hyperparameters consistently yields the highest and most reliable cross-dataset gains (e.g., up to +5% Dice in segmentation) (Playout et al., 14 May 2024, Gesnouin et al., 2022).
• Bayesian and uncertainty-aware architectures: Last-layer Bayesian inference (SVI), Monte Carlo dropout, and calibration-focused techniques can detect low-confidence predictions under domain shift, though calibration in-domain is often uncorrelated with cross-domain behavior (Gesnouin et al., 2022).
• Large-scale and mixed-style pretraining: Pretraining on diverse, wide-coverage sources (e.g. Sports1M for action, CTRPv2 for DRP, FGADR for segmentation) reduces error in cross-dataset scenarios (Gesnouin et al., 2022, Partin et al., 18 Mar 2025, Playout et al., 14 May 2024).
• Data augmentation: Explicit distortion-injection (e.g., Extrinsic Rotation Augmentation in FoVA-Depth), temporal and speed augmentation in rPPG, domain-specific transforms (color, geometry, synthesis) all mitigate overfitting to source idiosyncrasies and broaden the effective support (Lichy et al., 24 Jan 2024, Vance et al., 2023, Nadimpalli et al., 2022).
• Domain adaptation and instance conditioning: Architectural techniques like X-MIC’s instance-conditioned adapters, de-stylization by feature normalization (UniStyle), or deep RL-guided adaptive test-time augmentations increase transfer robustness (Kukleva et al., 28 Mar 2024, Lee et al., 2022, Nadimpalli et al., 2022).
• Feature selection, anomaly detection, and dataset pruning: Removing non-generalizable topics or artifacts, via LDA or mRMR-based inspection, and rigorous deduplication, can help prevent learning of domain-specific biases (Nejadgholi et al., 2020, Cantone et al., 15 Feb 2024).
• Label harmonization, multi-head, and ontology alignment models: Attempts to unify label taxonomies or employ multi-head architectures have yielded only modest improvements; graph-based transfer learning showed limited efficacy absent expert-driven harmonization (Jalocha et al., 18 Jul 2025).

5. Application Field Highlights

The cross-dataset generalization paradigm has been empirically probed in:

• Vision: pedestrian intent prediction (Gesnouin et al., 2022), synthetic-vs-real benchmarking (Song et al., 14 Sep 2025), gaze estimation with evidential fusion (Wang et al., 7 Sep 2024), depth estimation across FoV types (Lichy et al., 24 Jan 2024).
• Biomedical: retinal lesion segmentation under annotation style mismatches (Playout et al., 14 May 2024), drug response prediction (Partin et al., 18 Mar 2025), remote photoplethysmography heart-rate prediction (Vance et al., 2023).
• Security: ALPR across global datasets (Laroca et al., 2022), cybersecurity NER label unification (Jalocha et al., 18 Jul 2025), and network intrusion detection (Cantone et al., 15 Feb 2024).
• Language: NLI artifact reweighting (Zhang et al., 2019), and abuse detection under topic bias (Nejadgholi et al., 2020).
• Autonomous agents: trajectory prediction capitalizing on goal selection and graph neural structures (Grimm et al., 24 Jul 2025).

Each subfield has demonstrated both the generic barriers—distribution shifts, annotation conflicts, domain artifacts—and the potential of tailored advances (ensembles, data augmentation, architecture modifications) to partly overcome them.

6. Key Guidelines and Best Practices

Empirical and theoretical work converges on several practical recommendations:

• Always adopt cross-dataset/leave-one-dataset-out evaluation protocols for reporting real-world readiness.
• Characterize and cluster datasets by style, content, and annotation regime prior to merging.
• Restructure pretraining to maximize diversity and physiological realism; employ large, multi-source datasets where feasible.
• Integrate uncertainty estimation to reject low-confidence predictions under shift.
• Prefer ensemble and instance-conditioned strategies for deployment in uncertain domains.
• Use data- and feature-level anomaly detection and topic modeling to prune non-generalizable content.
• Embrace domain-adaptation, few-shot, and federated approaches for future advances.

Failure to address these best practices risks producing models that merely memorize domain-specific cues, yielding illusory performance gains that collapse upon deployment (Gesnouin et al., 2022, Playout et al., 14 May 2024, Partin et al., 18 Mar 2025, Cantone et al., 15 Feb 2024).

7. Limitations and Future Directions

Despite methodological advances, true cross-dataset generalization remains elusive for most domains. Persistent challenges include:

• Intractable label and data protocol discrepancies (especially in language and NER tasks) (Jalocha et al., 18 Jul 2025, Zhang et al., 2019).
• Insufficient annotation or sample diversity to cover the target’s full data manifold (Zhang et al., 15 Oct 2024, Playout et al., 14 May 2024).
• Varied effectiveness of adaptation and harmonization strategies, especially where deep domain structure diverges (Wang et al., 7 Sep 2024, Partin et al., 18 Mar 2025).

Promising future avenues include structured ontology harmonization, advanced adversarial and domain-invariant representation learning, meta-learning, large-scale synthetic data assessment via Generalized Cross-Validation (Song et al., 14 Sep 2025), and ongoing quantitative benchmarking of generalization gaps as core metrics.

Cross-dataset generalization represents both a foundational barrier and a driving motivation for modern robust machine learning. Its systematic paper enables the development of methods and protocols toward truly deployable models in dynamic, heterogeneous environments.