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An Analysis of RF Transfer Learning Behavior Using Synthetic Data
Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML).
Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation
The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP).
Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models
While RFML is expected to be a key enabler of future wireless standards, a significant challenge to the widespread adoption of RFML techniques is the lack of explainability in deep learning models.
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
To address this problem, we present an open source Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks from a broad range of viewing angles (-32. 5 degrees to 54. 0 degrees).
