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Found 5 papers, 4 papers with code
On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization
However, this algorithm still faces weaknesses that negatively affect the model accuracy and training stability, primarily due to a gradient imbalance between positive and negative samples.
On the Robustness of Fully-Spiking Neural Networks in Open-World Scenarios using Forward-Only Learning Algorithms
In the last decade, Artificial Intelligence (AI) models have rapidly integrated into production pipelines propelled by their excellent modeling performance.
Managing the unknown: a survey on Open Set Recognition and tangential areas
In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage.
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery
Besides performance, efficiency is a key design driver of technologies supporting vehicular perception.
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks
Specifically, this work presents a novel OoD detector that can identify whether test examples input to a Spiking Neural Network belong to the distribution of the data over which it was trained.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
