arXiv:2511.02933v1 Announce Type: new 
Abstract: Data augmentation is widely used in vision to introduce variation and mitigate overfitting, through enabling models to learn invariant properties, such as spatial invariance. However, these properties are not fully captured by data augmentation alone, since it attempts to learn the property on transformations of the training data only. We propose generative hints, a training methodology that directly enforces known invariances in the entire input space. Our approach leverages a generative model trained on the training set to approximate the input distribution and generate unlabeled images, which we refer to as virtual examples. These virtual examples are used to enforce functional properties known as hints. In generative hints, although the training dataset is fully labeled, the model is trained in a semi-supervised manner on both the classification and hint objectives, using the unlabeled virtual examples to guide the model in learning the desired hint. Across datasets, architectures, and loss functions, generative hints consistently outperform standard data augmentation when learning the same property. On popular fine-grained visual classification benchmarks, we achieved up to 1.78% top-1 accuracy improvement (0.63% on average) over fine-tuned models with data augmentation and an average performance boost of 1.286% on the CheXpert X-ray dataset.

تم اقتراح منهجية جديدة تُسمى التلميحات التوليدية لتعزيز زيادة البيانات في التعلم الآلي. تهدف هذه الطريقة إلى فرض عدم التغير المعروف مباشرةً على المدخلات بالكامل، بدلاً من الاعتماد فقط على تحويلات بيانات التدريب. هذا مهم لأنه قد يؤدي إلى نماذج أكثر قوة تفهم عدم التغير المكاني بشكل أفضل، مما يحسن الأداء ويقلل من الإفراط في التكيف في تطبيقات مختلفة.

Se ha propuesto una nueva metodología llamada pistas generativas para mejorar la augmentación de datos en el aprendizaje automático. Este enfoque busca imponer directamente invariancias conocidas en toda la entrada, en lugar de depender únicamente de las transformaciones de los datos de entrenamiento. Esto es significativo porque podría llevar a modelos más robustos que comprendan mejor la invariancia espacial, mejorando así el rendimiento y reduciendo el sobreajuste en diversas aplicaciones.

Une nouvelle méthodologie appelée indices génératifs a été proposée pour améliorer l'augmentation des données dans l'apprentissage automatique. Cette approche vise à appliquer directement des invariances connues sur l'ensemble de l'entrée, plutôt que de se fier uniquement aux transformations des données d'entraînement. Cela est important car cela pourrait conduire à des modèles plus robustes qui comprennent mieux l'invariance spatiale, améliorant ainsi les performances et réduisant le surapprentissage dans diverses applications.

A new methodology called generative hints has been proposed to enhance data augmentation in machine learning. This approach aims to directly enforce known invariances across the entire input, rather than relying solely on transformations of training data. This is significant because it could lead to more robust models that better understand spatial invariance, ultimately improving performance and reducing overfitting in various applications.

Generative Hints

arXiv:2507.06819v3 Announce Type: replace 
Abstract: Prototype models are an important method for explainable artificial intelligence (XAI) and interpretable machine learning. In this paper, we perform an in-depth analysis of a set of prominent prototype models including ProtoPNet, ProtoPool and PIPNet. For their assessment, we apply a comprehensive set of metrics. In addition to applying standard metrics from literature, we propose several new metrics to further complement the analysis of model interpretability. In our experimentation, we apply the set of prototype models on a diverse set of datasets including fine-grained classification, Non-IID settings and multi-label classification to further contrast the performance. Furthermore, we also provide our code as an open-source library (https://github.com/uos-sis/quanproto), which facilitates simple application of the metrics itself, as well as extensibility -- providing the option for easily adding new metrics and models.

تم إجراء تقييم شامل لشبكات الأعصاب النموذجية، مع التركيز على نماذج مثل ProtoPNet وProtoPool وPIPNet. تطبق الدراسة مجموعة متنوعة من المقاييس، بما في ذلك مقاييس جديدة اقترحها المؤلفون، لتقييم قابلية تفسير النموذج عبر مجموعات بيانات متنوعة، بما في ذلك مهام التصنيف الدقيق ومتعدد التسميات. الكود الخاص بهذه التقييمات متاح كمكتبة مفتوحة المصدر على GitHub.

Se ha realizado una evaluación integral de redes neuronales prototipo, centrándose en modelos como ProtoPNet, ProtoPool y PIPNet. El estudio aplica una variedad de métricas, incluidas algunas nuevas propuestas por los autores, para evaluar la interpretabilidad del modelo en diversos conjuntos de datos, incluidas tareas de clasificación fina y multi-etiqueta. El código para estas evaluaciones está disponible como una biblioteca de código abierto en GitHub.

Une évaluation complète des réseaux de neurones prototypes a été réalisée, en se concentrant sur des modèles tels que ProtoPNet, ProtoPool et PIPNet. L'étude applique une variété de métriques, y compris de nouvelles proposées par les auteurs, pour évaluer l'interprétabilité des modèles sur divers ensembles de données, y compris des tâches de classification fine et multi-label. Le code pour ces évaluations est disponible en tant que bibliothèque open-source sur GitHub.

A comprehensive evaluation of prototype neural networks has been conducted, focusing on models such as ProtoPNet, ProtoPool, and PIPNet. The study applies a variety of metrics, including new ones proposed by the authors, to assess model interpretability across diverse datasets, including fine-grained and multi-label classification tasks. The code for these evaluations is available as an open-source library on GitHub.

Comprehensive Evaluation of Prototype Neural Networks

arXiv:2511.16792v1 Announce Type: cross 
Abstract: Membership inference attacks (MIAs) against machine learning (ML) models aim to determine whether a given data point was part of the model training data. These attacks may pose significant privacy risks to individuals whose sensitive data were used for training, which motivates the use of defenses such as differential privacy, often at the cost of high accuracy losses. MIAs exploit the differences in the behavior of a model when making predictions on samples it has seen during training (members) versus those it has not seen (non-members). Several studies have pointed out that model overfitting is the major factor contributing to these differences in behavior and, consequently, to the success of MIAs. However, the literature also shows that even non-overfitted ML models can leak information about a small subset of their training data. In this paper, we investigate the root causes of membership inference vulnerabilities beyond traditional overfitting concerns and suggest targeted defenses. We empirically analyze the characteristics of the training data samples vulnerable to MIAs in models that are not overfitted (and hence able to generalize). Our findings reveal that these samples are often outliers within their classes (e.g., noisy or hard to classify). We then propose potential defensive strategies to protect these vulnerable samples and enhance the privacy-preserving capabilities of ML models. Our code is available at https://github.com/najeebjebreel/mia_analysis.

تثير هجمات استنتاج العضوية (MIA) ضد نماذج التعلم الآلي مخاوف كبيرة بشأن الخصوصية، حيث يمكنها تحديد ما إذا كانت نقاط بيانات معينة جزءًا من بيانات التدريب. تستكشف هذه الورقة نقاط الضعف في MIA التي تستمر حتى في النماذج غير المفرطة التكيف، مما يبرز الحاجة إلى تحسين الدفاعات بما يتجاوز الأساليب التقليدية مثل الخصوصية التفاضلية.

Los ataques de inferencia de pertenencia (MIA) contra modelos de aprendizaje automático han suscitado preocupaciones significativas sobre la privacidad, ya que pueden determinar si puntos de datos específicos formaron parte de los conjuntos de datos de entrenamiento. Este artículo investiga las vulnerabilidades a los MIA que persisten incluso en modelos no sobreajustados, destacando la necesidad de defensas mejoradas más allá de los métodos tradicionales como la privacidad diferencial.

Les attaques par inférence d'appartenance (MIA) contre les modèles d'apprentissage automatique soulèvent d'importantes préoccupations en matière de confidentialité, car elles peuvent déterminer si des points de données spécifiques ont été inclus dans les ensembles de données d'entraînement. Cet article explore les vulnérabilités aux MIA qui persistent même dans les modèles non surajustés, soulignant la nécessité d'améliorer les défenses au-delà des méthodes traditionnelles comme la confidentialité différentielle.

Membership inference attacks (MIAs) against machine learning models have raised significant privacy concerns, as they can determine if specific data points were included in training datasets. This paper explores vulnerabilities to MIAs that persist even in non-overfitted models, highlighting the need for improved defenses beyond traditional methods like differential privacy.

Generative Hints

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