Interference Mitigation for Automotive FMCW Radar Based on Contrastive Learning With Dilated Convolution

As one of the crucial sensors for environment sensing, frequency modulated continuous wave (FMCW) radars are widely used in modern vehicles for driving assistance/autonomous driving. However, the limited frequency bandwidth and the increasing number of equipped radar sensors would inevitably cause mutual interference, degrading target...

Meta-Learning with label noise: A step towards label few-shot meta-learning with label noise.

Few-shot learning presents the challenging problem of learning a task with only a few provided examples. Gradient-Based Meta-Learners (GBML) offer a solution for learning such few-shot problems. These learners approach the few-shot problem by learning an initial parameterization that requires only a few adaptation steps for new tasks. Although...