… self-paced learning is integrated into SMCT to rectify false pseudo labels and avoid error
accumulation. Concretely, the multi-label co-training … on six benchmark multi-label datasets and …

… a self-paced co-training of GNNs framework for solving semi-supervised graph node classification
problems. In this framework, base classifiers used for co-training are … via a self-paced …

… SELF-PACED LEARNING Self-paced learning (SPL) is … multiple graphs in a completely
self-paced way, and has certain … was introduced into self-paced multi label cooperative training (…

… [19], and Self-paced Multi-label Co-Training … co-training paradigm with multi-label data.
The first one is that it is difficult to construct two sufficient and redundant views on most multi-label …

… to the feedback of self-paced strategy. The combination of … the self-paced strategy from
overfitting, and self-paced strategy as “… two curriculums into the self-paced PLL method and obtain …

… multi-label learning methods require sufficient labeled training samples, because of the
large number of possible label sets. Co-training, … to combine multi-label learning with co-training. …

… Multi-label learning aims to solve classification problems where instances are associated …
In semi-supervised learning, co-training is successfully in augmenting the training data with …

… Inspired by [25], we apply a self-paced selection strategy to select these reliable pseudo-labels.
Firstly, we introduce an age parameter 𝜆 and initialize its value as 0 and progressively …

… TRAM formulates the multi-label classification as an optimization problem of estimating label
… COINS adapts co-training strategy to multi-label learning. Two classification models are …

… a curriculum learning strategy that learns a self-paced model. Besides, some studies are …
COIN [99] adapts the well-known co-training strategy to SS-MLC setting. In each co-training …