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Incremental Object-Based Novelty Detection with Feedback Loop
Simone Caldarella, Elisa Ricci, Rahaf Aljundi
Object-based Novelty Detection (ND) aims to identify unknown objects that do not belong to classes seen during training by an object detection model. The task is particularly cruci…
OOD Aware Supervised Contrastive Learning
Soroush Seifi, Daniel Olmeda Reino, Nikolay Chumerin +1
Out-of-Distribution (OOD) detection is a crucial problem for the safe deployment of machine learning models identifying samples that fall outside of the training distribution, i.e.…
Overcoming Generic Knowledge Loss with Selective Parameter Update
Wenxuan Zhang, Paul Janson, Rahaf Aljundi +1
Foundation models encompass an extensive knowledge base and offer remarkable transferability. However, this knowledge becomes outdated or insufficient over time. The challenge lies…
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
Nader Asadi, MohammadReza Davari, Sudhir Mudur +2
In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent best-performing methods utilize various…
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning
Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino +2
In Class-Incremental Learning (CIL) an image classification system is exposed to new classes in each learning session and must be updated incrementally. Methods approaching this pr…
Calibrated Out-of-Distribution Detection with a Generic Representation
Tomas Vojir, Jan Sochman, Rahaf Aljundi +1
Out-of-distribution detection is a common issue in deploying vision models in practice and solving it is an essential building block in safety critical applications. Most of the ex…