Discriminability-Driven Spatial-Channel Selection with Gradient Norm for Drone Signal OOD Detection
We propose a drone signal out-of-distribution (OOD) detection algorithm based on discriminability-driven spatial-channel selection with a gradient norm. Time-frequency image features are adaptively weighted along both spatial and channel dimensions by quantifying inter-class similarity and variance based on protocol-specific time-frequency characteristics. Subsequently, a gradient-norm metric is introduced to measure perturbation sensitivity for capturing the inherent instability of OOD samples, which is then fused with energy-based scores for joint inference. Simulation results demonstrate that the proposed algorithm provides superior discriminative power and robust performance via SNR and various drone types.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs
Class activation map (CAM) has been widely studied for visual explanation of the internal working mechanism of convolutional neural networks. The key of existing CAM-based methods is to compute effective weights to combi…
Discriminability-Driven Channel Selection for Out-of-Distribution Detection
Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world environments. Activation-based methods are a key approach in OOD detection working to mitigate overconfident predic…
channel selectionOut-of-Distribution DetectionOut of Distribution (OOD) DetectionRS-CA-HSICT: A Residual and Spatial Channel Augmented CNN Transformer Framework for Monkeypox Detection
This work proposes a hybrid deep learning approach, namely Residual and Spatial Learning based Channel Augmented Integrated CNN-Transformer architecture, that leverages the strengths of CNN and Transformer towards enhanc…
A Domain-Informed Multi-Objective Framework for EEG Channel Selection in Motor Imagery BCIs
Motor imagery (MI) classification using electroencephalography (EEG) signals is essential for advancing brain-computer interfaces (BCIs). Traditional EEG channel selection methods often face limitations, such as dependen…
Learning Topology-Driven Multi-Subspace Fusion for Grassmannian Deep Network
Grassmannian manifold offers a powerful carrier for geometric representation learning by modelling high-dimensional data as low-dimensional subspaces. However, existing approaches predominantly rely on static single-subs…
Representation Learning3D Action Recognition