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On the Connection Between Neural Activations and Uncertainty in Object Detection Transformers Using Topological Data Analysis
Javier Del Ser;Aitor Martinez-Seras;Ibai Laña;Miren Nekane Bilbao;Panagiotis Fafoutellis;Eleni I. Vlahogianni
2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2024 | Conference Paper |
Over the years, deep neural networks have achieved unrivaled levels of predictive performance in detecting and identifying objects from visual data, elevating them as a core technology for vehicular perception and automated driving. Recently, the research interest has drifted from performance-driven advances towards the improvement of the reliability and robustness of neural object detectors when ...Show More
Balancing Performance, Efficiency and Robustness in Open-World Machine Learning via Evolutionary Multi-objective Model Compression
Javier Del Ser;Aitor Martinez-Seras;Miren Nekane Bilbao;Jesus L. Lobo;Ibai Laña;Francisco Herrera
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
When deploying machine learning models on resource-constrained hardware, reducing the memory footprint required by the model without compromising its performance is critical. Moreover, in open-world scenarios models often operate in dynamic and unpredictable environments where the data distribution evolves over time. Robust models can generalize well to unforeseen circumstances, including out-of-d...Show More
Advancing towards Safe Reinforcement Learning over Sparse Environments with Out-of-Distribution Observations: Detection and Adaptation Strategies
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Safety in AI-based systems is among the highest research priorities, particularly when such systems are deployed in real-world scenarios subject to uncertainties and unpredictable inputs. Among them, the presence of long-tailed stimuli (Out-of-Distribution data, OoD) has captured much interest in recent times, giving rise to many proposals over the years to detect unfamiliar inputs to the model an...Show More
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Besides performance, efficiency is a key design driver of technologies supporting vehicular perception. Indeed, a well-balanced trade-off between performance and energy consumption is crucial for the sustainability of autonomous vehicles. In this context, the diversity of real-world contexts in which autonomous vehicles can operate motivates the need for empowering perception models with the capab...Show More
Can Post-hoc Explanations Effectively Detect Out-of-Distribution Samples?
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Today there is consensus around the importance of explainability as a mandatory feature in practical deployments of Artificial Intelligence (AI) models. Most research activity reported so far in the eXplainable AI (XAI) research arena has stressed on proposing new techniques for eliciting such explanations, together with different approaches for measuring their effectivity to increase the trustwor...Show More
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