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Found 5 papers, 2 papers with code
CBA: Improving Online Continual Learning via Continual Bias Adaptor
Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams.
Imbalanced Semi-supervised Learning with Bias Adaptive Classifier
The core idea is to automatically assimilate the training bias caused by class imbalance via the bias adaptive classifier, which is composed of a novel bias attractor and the original linear classifier.
Diagnosing Batch Normalization in Class Incremental Learning
Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL).
Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship
Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data.
Residual Moment Loss for Medical Image Segmentation
Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segmentation.
