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Found 7 papers, 2 papers with code
I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning
Online task-free continual learning (OTFCL) is a more challenging variant of continual learning which emphasizes the gradual shift of task boundaries and learns in an online mode.
Few-shot Online Anomaly Detection and Segmentation
Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to enhance the model's performance.
CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning
Besides, to address the classifier bias towards the new classes, we propose a novel approach to generate the pseudo-features to correct the classifier.
Knowledge Restore and Transfer for Multi-label Class-Incremental Learning
Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied.
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning
Deep neural networks suffer from catastrophic forgetting in class incremental learning, where the classification accuracy of old classes drastically deteriorates when the networks learn the knowledge of new classes.
Deep Class Incremental Learning from Decentralized Data
In this paper, we focus on a new and challenging decentralized machine learning paradigm in which there are continuous inflows of data to be addressed and the data are stored in multiple repositories.
Few-Shot Class-Incremental Learning
FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones.
Ranked #8 on
Few-Shot Class-Incremental Learning
on CIFAR-100
(Average Accuracy metric)
