2021/11/04 by Muhammad Rifki Kurniawan, Xing Wei, Kurniawan, Muhammad Rifki +3
Computer Science · Medicine · #Domain Adaptation and Few-Shot Learning #Multimodal Machine Learning Applications #COVID-19 diagnosis using AI
paper · pdf · doi:10.48550/arxiv.2111.02757
Online continual learning in the wild is a very difficult task in machine learning. Non-stationarity in online continual learning potentially brings about catastrophic forgetting in neural networks. Specifically, online continual learning for autonomous driving with SODA10M dataset exhibits extra problems on extremely long-tailed distribution with continuous distribution shift. To address these problems, we propose multiple deep metric representation learning via both contrastive and supervised contrastive learning alongside soft labels distillation to improve model generalization. Moreover, we exploit modified class-balanced focal loss for sensitive penalization in class imbalanced and hard-easy samples. We also store some samples under guidance of uncertainty metric for rehearsal and perform online and periodical memory updates. Our proposed method achieves considerable generalization with average mean class accuracy (AMCA) 64.01% on validation and 64.53% AMCA on test set.