Learning Robust Representations for Automatic Target Recognition
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses, RF systems are susceptible to attacks masking true target characteristics from accurate identification. In this work, we evaluate different techniques for building robust classification architectures exploiting learned physical structure in received synthetic aperture radar signals of simulated 3D targets.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationRobust classificationSimilar Papers 제목 키워드 기반
A Dual-Polarization Feature Fusion Network for Radar Automatic Target Recognition Based On HRRP Sequence
Recent advances in radar automatic target recognition (RATR) techniques utilizing deep neural networks have demonstrated remarkable performance, largely due to their robust generalization capabilities. To address the cha…
Spatial-Scale Aligned Network for Fine-Grained Recognition
Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance. In this paper, we pro…
Fine-Grained Visual RecognitionStable Distillation: Regularizing Continued Pre-training for Low-Resource Automatic Speech Recognition
Continued self-supervised (SSL) pre-training for adapting existing SSL models to the target domain has shown to be extremely effective for low-resource Automatic Speech Recognition (ASR). This paper proposes Stable Disti…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech RecognitionAutomatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations
Self-Supervised Learning (SSL) has allowed leveraging large amounts of unlabeled speech data to improve the performance of speech recognition models even with small annotated datasets. Despite this, speech SSL representa…
Data AugmentationDomain AdaptationSelf-Supervised Learningspeech-recognition+1Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation
Recent advancements in textless speech-to-speech translation systems have been driven by the adoption of self-supervised learning techniques. Although most state-of-the-art systems adopt a similar architecture to transfo…
Automatic Speech RecognitionEmotion Recognitionfeature selectionResynthesis+7