arXiv:2505.08423v4 Announce Type: replace 
Abstract: Facial recognition systems have achieved remarkable success by leveraging deep neural networks, advanced loss functions, and large-scale datasets. However, their performance often deteriorates in real-world scenarios involving low-quality facial images. Such degradations, common in surveillance footage or standoff imaging include low resolution, motion blur, and various distortions, resulting in a substantial domain gap from the high-quality data typically used during training. While existing approaches attempt to address robustness by modifying network architectures or modeling global spatial transformations, they frequently overlook local, non-rigid deformations that are inherently present in real-world settings. In this work, we introduce \textbf{DArFace}, a \textbf{D}eformation-\textbf{A}ware \textbf{r}obust \textbf{Face} recognition framework that enhances robustness to such degradations without requiring paired high- and low-quality training samples. Our method adversarially integrates both global transformations (e.g., rotation, translation) and local elastic deformations during training to simulate realistic low-quality conditions. Moreover, we introduce a contrastive objective to enforce identity consistency across different deformed views. Extensive evaluations on low-quality benchmarks including TinyFace, IJB-B, and IJB-C demonstrate that DArFace surpasses state-of-the-art methods, with significant gains attributed to the inclusion of local deformation modeling.

تقدم دراسة جديدة طريقة DArFace، التي تهدف إلى تعزيز قوة أنظمة التعرف على الوجه عند التعامل مع الصور منخفضة الجودة. وهذا مهم لأن الأنظمة التقليدية تواجه صعوبات مع مشكلات مثل ضبابية الحركة وانخفاض الدقة، والتي تكون شائعة في التطبيقات الواقعية مثل المراقبة. من خلال معالجة هذه التحديات، يمكن أن تحسن DArFace موثوقية تقنية التعرف على الوجه، مما يجعلها أكثر فعالية في المواقف الحرجة.

Un nuevo estudio presenta DArFace, un método diseñado para mejorar la robustez de los sistemas de reconocimiento facial al tratar con imágenes de baja calidad. Esto es significativo porque los sistemas tradicionales luchan con problemas como el desenfoque por movimiento y la baja resolución, que son comunes en aplicaciones del mundo real como la vigilancia. Al abordar estos desafíos, DArFace podría mejorar la fiabilidad de la tecnología de reconocimiento facial, haciéndola más efectiva en situaciones críticas.

Une nouvelle étude présente DArFace, une méthode conçue pour améliorer la robustesse des systèmes de reconnaissance faciale face aux images de faible qualité. Cela est important car les systèmes traditionnels ont du mal avec des problèmes tels que le flou de mouvement et la faible résolution, qui sont courants dans des applications réelles comme la surveillance. En s'attaquant à ces défis, DArFace pourrait améliorer la fiabilité de la technologie de reconnaissance faciale, la rendant plus efficace dans des situations critiques.

A new study introduces DArFace, a method designed to enhance the robustness of facial recognition systems when dealing with low-quality images. This is significant because traditional systems struggle with issues like motion blur and low resolution, which are common in real-world applications such as surveillance. By addressing these challenges, DArFace could improve the reliability of facial recognition technology, making it more effective in critical situations.

DArFace: Deformation Aware Robustness for Low Quality Face Recognition

arXiv:2601.08100v1 Announce Type: new 
Abstract: Understanding the generalization behavior of deep neural networks remains a fundamental challenge in modern statistical learning theory. Among existing approaches, PAC-Bayesian norm-based bounds have demonstrated particular promise due to their data-dependent nature and their ability to capture algorithmic and geometric properties of learned models. However, most existing results rely on isotropic Gaussian posteriors, heavy use of spectral-norm concentration for weight perturbations, and largely architecture-agnostic analyses, which together limit both the tightness and practical relevance of the resulting bounds. To address these limitations, in this work, we propose a unified framework for PAC-Bayesian norm-based generalization by reformulating the derivation of generalization bounds as a stochastic optimization problem over anisotropic Gaussian posteriors. The key to our approach is a sensitivity matrix that quantifies the network outputs with respect to structured weight perturbations, enabling the explicit incorporation of heterogeneous parameter sensitivities and architectural structures. By imposing different structural assumptions on this sensitivity matrix, we derive a family of generalization bounds that recover several existing PAC-Bayesian results as special cases, while yielding bounds that are comparable to or tighter than state-of-the-art approaches. Such a unified framework provides a principled and flexible way for geometry-/structure-aware and interpretable generalization analysis in deep learning.

تشير دراسة جديدة إلى إطار موحد لـ PAC-Bayes للحدود العامة المعتمدة على المعايير، حيث تتناول التحديات المتعلقة بفهم سلوك التعميم في الشبكات العصبية العميقة. تعيد هذه الدراسة صياغة اشتقاق هذه الحدود كمشكلة تحسين عشوائي على توزيعات غاوسية غير متساوية، بهدف تعزيز الصلة العملية للنتائج.

Un nuevo estudio propone un marco PAC-Bayes unificado para los límites de generalización basados en normas, abordando los desafíos de comprender el comportamiento de generalización de las redes neuronales profundas. La investigación reformula la derivación de estos límites como un problema de optimización estocástica sobre posteriors gaussianos anisotrópicos, con el objetivo de mejorar la relevancia práctica de los resultados.

Une nouvelle étude propose un cadre PAC-Bayésien unifié pour les bornes de généralisation basées sur les normes, abordant les défis de la compréhension du comportement de généralisation des réseaux de neurones profonds. La recherche reformule la dérivation de ces bornes comme un problème d'optimisation stochastique sur des postérieurs gaussiens anisotropes, visant à améliorer la pertinence pratique des résultats.

A new study proposes a unified PAC-Bayesian framework for norm-based generalization bounds, addressing the challenges of understanding deep neural networks' generalization behavior. The research reformulates the derivation of these bounds as a stochastic optimization problem over anisotropic Gaussian posteriors, aiming to enhance the practical relevance of the results.

Towards A Unified PAC-Bayesian Framework for Norm-based Generalization Bounds

arXiv:2601.07944v1 Announce Type: new 
Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex and large-scale predictive problems. The recent success of deep neural networks and foundation models has now given rise to a new paradigm in statistical modeling, in which Bayesian inference can be amortized through large-scale learned predictors. In amortized inference, substantial computation is invested upfront to train a neural network that can subsequently produce approximate posterior or predictions at negligible marginal cost across a wide range of tasks. At deployment, amortized inference offers substantial computational savings compared with traditional Bayesian procedures, which generally require repeated likelihood evaluations or Monte Carlo simulations for predictions for each new dataset.
  Despite the growing popularity of amortized inference, its statistical interpretation and its role within Bayesian inference remain poorly understood. This paper presents statistical perspectives on the working principles of several major neural architectures, including feedforward networks, Deep Sets, and Transformers, and examines how these architectures naturally support amortized Bayesian inference. We discuss how these models perform structured approximation and probabilistic reasoning in ways that yield controlled generalization error across a wide range of deployment scenarios, and how these properties can be harnessed for Bayesian computation. Through simulation studies, we evaluate the accuracy, robustness, and uncertainty quantification of amortized inference under varying signal-to-noise ratios and distributional shifts, highlighting both its strengths and its limitations.

أظهرت دراسة حديثة فعالية الاستدلال المدعوم في الإحصاءات البايزية، خاصة تحت تغيرات نسبة الإشارة إلى الضوضاء وتحولات التوزيع. تستخدم هذه الطريقة الشبكات العصبية العميقة لتبسيط عملية الاستدلال، مما يسمح بتوفير كبير في الحوسبة مقارنة بالأساليب البايزية التقليدية التي تتطلب تقييمات واسعة لاحتمالية.

Un estudio reciente ha evaluado la efectividad de la inferencia amortizada en estadísticas bayesianas, particularmente bajo variaciones en la relación señal-ruido y cambios en la distribución. Este método aprovecha redes neuronales profundas para agilizar el proceso de inferencia, permitiendo ahorros computacionales significativos en comparación con los enfoques bayesianos tradicionales que requieren evaluaciones extensas de verosimilitud.

Une étude récente a évalué l'efficacité de l'inférence amortie dans les statistiques bayésiennes, en particulier sous des variations de rapport signal-bruit et des changements de distribution. Cette méthode utilise des réseaux de neurones profonds pour rationaliser le processus d'inférence, permettant des économies computationnelles significatives par rapport aux approches bayésiennes traditionnelles qui nécessitent des évaluations de vraisemblance étendues.

A recent study has assessed the effectiveness of amortized inference in Bayesian statistics, particularly under varying signal-to-noise ratios and distribution shifts. This method leverages deep neural networks to streamline the inference process, allowing for significant computational savings compared to traditional Bayesian approaches that require extensive likelihood evaluations.

A Statistical Assessment of Amortized Inference Under Signal-to-Noise Variation and Distribution Shift

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DArFace: Deformation Aware Robustness for Low Quality Face Recognition

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