6 papers
Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories
Achref Jaziri, Martin Rogmann, Martin Mundt +1
Detecting out-of-distribution (OOD) data is critical for machine learning, be it for safety reasons or to enable open-ended learning. However, beyond mere detection, choosing an ap…
A Simple Baseline for Stable and Plastic Neural Networks
Ãtienne Künzel, Achref Jaziri, Visvanathan Ramesh
Continual learning in computer vision requires that models adapt to a continuous stream of tasks without forgetting prior knowledge, yet existing approaches often tip the balance h…
synth-dacl: Does Synthetic Defect Data Enhance Segmentation Accuracy and Robustness for Real-World Bridge Inspections?
Johannes Flotzinger, Fabian Deuser, Achref Jaziri +4
Adequate bridge inspection is increasingly challenging in many countries due to growing ailing stocks, compounded with a lack of staff and financial resources. Automating the key t…
Uncertainty-Aware Decomposed Hybrid Networks
Sina Ditzel, Achref Jaziri, Iuliia Pliushch +1
The robustness of image recognition algorithms remains a critical challenge, as current models often depend on large quantities of labeled data. In this paper, we propose a hybrid…
Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion
Achref Jaziri, Etienne Künzel, Visvanathan Ramesh
A continual learning agent builds on previous experiences to develop increasingly complex behaviors by adapting to non-stationary and dynamic environments while preserving previous…
Representation Learning in a Decomposed Encoder Design for Bio-inspired Hebbian Learning
Achref Jaziri, Sina Ditzel, Iuliia Pliushch +1
Modern data-driven machine learning system designs exploit inductive biases in architectural structure, invariance and equivariance requirements, task-specific loss functions, and…