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APPLICATION OF GENERATIVE MODELS FOR ENHANCING THE ROBUSTNESS OF VISUAL DATA INTERPRETATION UNDER UNCERTAINTY
08.06.2025 15:06
[1. Information systems and technologies]
Author: Vitalii Viktorovych Vynnychenko, PhD student, State Higher Educational Institution “Uzhhorod National University”, Uzhhorod
ORCID: 0000-0002-8522-7348 Vitalii Viktorovych
Introduction
Real-world computer-vision systems often encounter noise, occlusions, lighting variation, and sensor degradation. Conventional convolutional neural networks achieve impressive accuracy on curated benchmarks but degrade sharply when the test distribution diverges from the training data. Such failures threaten safety in autonomous driving, medical imaging, industrial inspection, and surveillance.
Generative models—variational autoencoders, generative adversarial networks, and more recently diffusion-based approaches—model the full data distribution rather than a direct label mapping [1]. By reconstructing plausible clean images, sampling diverse variants, and exposing distributional likelihoods, these models offer a principled way to pre-condition corrupted observations and to quantify epistemic and aleatoric uncertainty. This paper advances a theoretical perspective on integrating generative models into vision pipelines to improve robustness without reliance on handcrafted defences or exhaustive data augmentation.
Theoretical Foundations
Robustness and Distributional Shift
Robustness denotes a model’s ability to maintain predictive accuracy when the input distribution shifts. In practice, shift arises from changes in weather, camera hardware, or acquisition protocols. Classical empirical risk minimisation minimises average loss on the training distribution but provides no guarantee for unseen conditions [2]. The gap between true risk and empirical risk widens as the shift increases.
Generative Modelling as Distribution Approximation
By explicitly estimating the data density, generative models project corrupted inputs onto the manifold of likely clean images. This projection narrows the divergence between training and test distributions. Moreover, ensembles of generated reconstructions reveal regions of high epistemic uncertainty; large variance signals unfamiliar content where the classifier should abstain or defer.
Uncertainty Quantification
Aleatoric uncertainty stems from intrinsic noise in the data, while epistemic uncertainty reflects limited knowledge of the model. A generative module suppresses aleatoric noise through reconstruction and exposes epistemic uncertainty via diverse sampling [3]. Downstream decision modules can incorporate this information through calibrated confidence scores or threshold-based rejection.
Proposed Theoretical Framework
1.Generative Front-End
A diffusion model reconstructs a clean estimate of each input image and produces multiple stochastic variants. The mean of these variants serves as a denoised image; their pixel-wise variance forms an uncertainty map.
2.Task Network
A classifier or segmenter consumes the concatenation of the reconstructed image, the raw observation, and the uncertainty map. Joint training aligns the generative and discriminative objectives: the task network minimises predictive loss while the generative module minimises reconstruction loss. Balancing these objectives encourages latent features that are both informative and invariant to corruption.
3.Shift Estimation and Regularisation
The generative model estimates the likelihood of each observation. Low likelihood indicates an out-of-distribution sample, prompting the system to lower its confidence. A regularisation term penalises large divergence between latent posterior and a chosen prior, constraining the learned representation to remain close to the clean data manifold [4].
4.On-the-Fly Corruption Synthesis
During training the framework applies synthetic weather effects, compression artefacts, and sensor noise to each mini-batch. Because the generative model learns to invert these corruptions, the overall system gains resilience without explicit enumeration of every possible test condition [5].
Analytical Discussion
Advantages
•Principled Denoising: Reconstruction through a learned prior removes noise while preserving semantics.
•Explicit Uncertainty: Variance across reconstructions offers transparent confidence estimates rather than heuristic metrics.
•General-Purpose Defence: The approach addresses a broad spectrum of corruptions without specialised augmentations or adversarial training.
Limitations
•Computational Expense: Generative models, particularly diffusion-based ones, require significant training and inference resources.
•Potential Over-Smoothing: Aggressive denoising may erase fine details critical for certain tasks, such as micro-lesion detection.
•Dependency on Prior Quality: If the generative prior fails to capture key modes of the data distribution, reconstruction can introduce artifacts or bias.
Potential Applications
•Medical Imaging: Low-dose CT and MRI reconstruction benefits from noise suppression while uncertainty maps highlight regions requiring radiologist review.
•Autonomous Vehicles: Robust perception in fog, rain, or dusk improves safety margins; confidence scores guide fallback strategies.
•Industrial Inspection: Generative priors enable reliable defect detection under variable lighting and camera wear without constant recalibration.
•Remote Sensing: Satellite imagery often suffers from atmospheric distortion; reconstruction aligns observations with clean training distributions, enhancing land-use classification.
Conclusion
Integrating a generative front-end with a discriminative back-end provides a theoretically grounded pathway to robust visual inference under uncertainty. Reconstruction reduces aleatoric noise, sampling reveals epistemic uncertainty, and joint optimisation aligns latent representations with task objectives. The approach avoids brittle, domain-specific defences and delivers transparent confidence estimates essential for risk-aware deployment. Future research should seek computationally efficient training strategies, explore knowledge distillation to lightweight student models, and extend the framework to multimodal and multispectral data.
References
1.Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y. Generative adversarial nets // Advances in Neural Information Processing Systems 27 : proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS 2014), 8–13 Dec 2014, Montréal, Canada. – 2014. – Режим доступу: https://arxiv.org/abs/1406.2661 (дата звернення: 30.05.2025). – (англ.).
2.Hendrycks D., Dietterich T. Benchmarking neural network robustness to common corruptions and perturbations // Proceedings of the 8th International Conference on Learning Representations (ICLR 2019), 6–9 May 2019, New Orleans, USA. – 2019. – Режим доступу: https://arxiv.org/abs/1903.12261 (дата звернення: 30.05.2025). – (англ.).
3.Kingma D. P., Welling M. Auto-encoding variational Bayes [Електронний ресурс]. – Режим доступу: https://arxiv.org/abs/1312.6114 (дата звернення: 30.05.2025). – (англ.).
4.Ovadia Y., Fertig E., Ren J., Nado Z., Sculley D., Nowozin S., Dillon J., Lakshminarayanan B., Snoek J. Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift // Advances in Neural Information Processing Systems 32 : proceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019), 8–14 Dec 2019, Vancouver, Canada. – 2019. – Режим доступу: https://proceedings.neurips.cc/paper/2019/file/9716732c7c02731a504a0a73b9058b1d-Paper.pdf (дата звернення: 30.05.2025). – (англ.).
5.Song Y., Sohl-Dickstein J.Score-based generative modeling through stochastic differential equations // Proceedings of the 9th International Conference on Learning Representations (ICLR 2021), Virtual Event, 3–7 May 2021. – 2021. – Режим доступу: https://arxiv.org/abs/2011.13456 (дата звернення: 30.05.2025). – (англ.).
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Academic advisor: Serhii Volodymyrovych Mashtalir, Doctor of Technical Sciences, Professor, State Higher Educational Institution “Uzhhorod National University”, Uzhhorod
Ця робота ліцензується відповідно до Creative Commons Attribution 4.0 International License
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