Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Nouboukpo, A.; Allili, M. S.
Semi-supervised flow-augmented Gaussian mixture VAEs with weighted contrastive learning for out-of-distribution detection Journal Article
In: Knowledge-Based Systems, vol. 347, 2026, ISSN: 09507051 (ISSN).
Abstract | Links | BibTeX | Tags: Auto encoders, Benchmarking, Classification (of information), Clusterings, Contrastive Learning, Flow based, Flow-based GMM clustering, Gaussian distribution, Gaussian-mixtures, Learning algorithms, Learning systems, Out-of-distribution detection, Semantics, Semi-supervised, Semi-supervised learning, uncertainty, Variational autoencoder, Variational autoencoders (VAEs)
@article{nouboukpoSemisupervisedFlowaugmentedGaussian2026,
title = {Semi-supervised flow-augmented Gaussian mixture VAEs with weighted contrastive learning for out-of-distribution detection},
author = {A. Nouboukpo and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105040505017?origin=resultslist},
doi = {10.1016/j.knosys.2026.116312},
issn = {09507051 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Knowledge-Based Systems},
volume = {347},
publisher = {Elsevier B.V.},
abstract = {Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning systems, particularly in high-stakes domains where identifying inputs that deviate from the in-distribution (ID) is critical. However, in real-world scenarios, true OOD data are typically unavailable at training time, and only auxiliary datasets acting as imperfect proxies can be used. This makes fully supervised approaches impractical and potentially biased toward specific anomaly types. To address this limitation, we propose FLoW-ssGMVAE, a semi-supervised generative–discriminative framework that leverages limited proxy anomaly supervision together with abundant unlabeled data for robust OOD detection. Our model integrates a flow-augmented Gaussian Mixture VAE, enabling flexible latent modeling that captures nonlinear and multimodal intra-class distributions. Unlike standard VAEs with unimodal Gaussian priors, it uses class-conditional normalizing flows to better represent complex ID and OOD variability. To further refine the latent space and strengthen ID/OOD separation, our model integrates a weighted contrastive learning objective guided by sample-wise attention scores derived from likelihood uncertainty. This mechanism emphasizes ambiguous or hard-to-classify instances, reinforcing semantic boundaries and reducing false detections. By combining expressive generative modeling with uncertainty-aware discriminative training, our method constructs a structured and interpretable latent space that reliably identifies OOD samples under limited supervision. Experiments on standard benchmarks validate the effectiveness and scalability of our approach, demonstrating state-of-the-art performance with reasonable computational cost. Our implementation will be available at: FLoW-ssGMVAE. © 2026},
keywords = {Auto encoders, Benchmarking, Classification (of information), Clusterings, Contrastive Learning, Flow based, Flow-based GMM clustering, Gaussian distribution, Gaussian-mixtures, Learning algorithms, Learning systems, Out-of-distribution detection, Semantics, Semi-supervised, Semi-supervised learning, uncertainty, Variational autoencoder, Variational autoencoders (VAEs)},
pubstate = {published},
tppubtype = {article}
}
Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning systems, particularly in high-stakes domains where identifying inputs that deviate from the in-distribution (ID) is critical. However, in real-world scenarios, true OOD data are typically unavailable at training time, and only auxiliary datasets acting as imperfect proxies can be used. This makes fully supervised approaches impractical and potentially biased toward specific anomaly types. To address this limitation, we propose FLoW-ssGMVAE, a semi-supervised generative–discriminative framework that leverages limited proxy anomaly supervision together with abundant unlabeled data for robust OOD detection. Our model integrates a flow-augmented Gaussian Mixture VAE, enabling flexible latent modeling that captures nonlinear and multimodal intra-class distributions. Unlike standard VAEs with unimodal Gaussian priors, it uses class-conditional normalizing flows to better represent complex ID and OOD variability. To further refine the latent space and strengthen ID/OOD separation, our model integrates a weighted contrastive learning objective guided by sample-wise attention scores derived from likelihood uncertainty. This mechanism emphasizes ambiguous or hard-to-classify instances, reinforcing semantic boundaries and reducing false detections. By combining expressive generative modeling with uncertainty-aware discriminative training, our method constructs a structured and interpretable latent space that reliably identifies OOD samples under limited supervision. Experiments on standard benchmarks validate the effectiveness and scalability of our approach, demonstrating state-of-the-art performance with reasonable computational cost. Our implementation will be available at: FLoW-ssGMVAE. © 2026
2.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Proceedings Article
In: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., pp. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valem_contrastive_2025,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218461565&doi=10.1007%2f978-3-031-77392-1_5&partnerID=40&md5=cf885303646c3b1a4f4eacb87d02a2b6},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
3.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Proceedings Article
In: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., pp. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valemContrastiveLossBased2025a,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/pages/publications/85218461565?origin=resultslist},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
4.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Proceedings Article
In: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., pp. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valemContrastiveLossBased2025,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218461565&doi=10.1007%2f978-3-031-77392-1_5&partnerID=40&md5=cf885303646c3b1a4f4eacb87d02a2b6},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.