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Found 5 papers, 2 papers with code
Confidence Self-Calibration for Multi-Label Class-Incremental Learning
The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable.
Variational Continual Test-Time Adaptation
At the source stage, we transform a pre-trained deterministic model into a Bayesian Neural Network (BNN) via a variational warm-up strategy, injecting uncertainties into the model.
Multi-Label Continual Learning using Augmented Graph Convolutional Network
In contrast, the inter-task relationships leverage hard and soft labels from data and a constructed expert network.
Class-Incremental Lifelong Learning in Multi-Label Classification
This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream.
AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognition
The key challenges of LML image recognition are the construction of label relationships on Partial Labels of training data and the Catastrophic Forgetting on old classes, resulting in poor generalization.
