WebSep 28, 2024 · Complementary-label learning (CLL) is a common application in the scenario of weak supervision. However, in real-world datasets, CLL encounters class-imbalanced training samples, where the quantity of samples of one class is significantly lower than those of other classes. Unfortunately, existing CLL approaches have yet to …

Learning with Biased Complementary Labels SpringerLink

WebDec 13, 2024 · Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the … WebMay 18, 2024 · In our framework, CE and robust loss play complementary roles in a joint learning objective as per their learning sufficiency and robustness properties … custom shirts greeley mall

Learning from Complementary Labels - NIPS

WebTo mitigate this problem, we propose a novel active learning setting, named active learning with complementary labels (ALCL). The ALCL learners only ask yes/no questions in some classes. After receiving answers from annotators, ALCL learners get a few supervised data and more training instances with complementary labels, which only specify one ... WebJournal [TPAMI] [SEU PALM Lab] Adaptive graph guided disambiguation for partial label learning. [TPAMI] Deep Partial Multi-View Learning. [TKDD] [SEU PALM Lab] … WebComplementary-Label Learning. This repository gives the implementation for complementary-label learning from the ICML 2024 paper [1], the ECCV 2024 paper [2], and the NeurIPS 2024 paper [3].. Requirements. Python … custom shirts fort wayne