Web14 de abr. de 2024 · We comprehensively discuss the long-tailed time series classification learning and construct three corresponding long-tailed datasets. To the best of our … WebTherefore, long-tailed classification is indispensable for training deep models at scale. Recent work Liu et al. (); Zhou et al. (); Kang et al. starts to fill in the performance gap between class-balanced and long-tailed datasets, while new long-tailed benchmarks are springing up such as Long-tailed CIFAR-10/-100 Cao et al. (); Zhou et al. (), ImageNet …
Long-Tailed Time Series Classification via Feature Space Rebalancing
Web1 de set. de 2024 · Existing methods of long-tailed classification mainly focus on re-sampling, re-weighting, and transfer learning. Although class imbalance learning can yield better long-tailed classification performance, the feature representative ability of the feature extraction network is damaged to a certain extent. WebAdversarial Robustness under Long-Tailed Distribution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 8659--8668. Google Scholar Cross Ref; Liuyu Xiang, Guiguang Ding, and Jungong Han. 2024. Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification. rose glory bower
Long-Tailed Classification by Keeping the Good and Removing …
Web6 de jan. de 2024 · This paper proposes a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME), inspired by the observation that networks trained on less imbalanced subsets of the distribution often yield better performances than their jointly-trained counterparts. In real-world scenarios, data tends … Web1 de dez. de 2024 · Long-tailed distribution learning is a particular classification task in machine learning and has been widely studied [15], [18], [39]. For instance, Yang et al. [42] proposed a scalable algorithm based on image retrieval and superpixel matching for application to scene analysis, which employs tail classes to achieve a semantic … WebFor natural language processing (NLP) ‘text-to-text’ tasks, prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources. However, this methodology is being critiqued for: exceptional compute and pretraining data requirements; diminishing returns on both large and small datasets; and importantly, favourable … storage units wetumpka al