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Found 5 papers, 1 papers with code
AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data
Federated Continual Learning (FCL) enables distributed clients to collaboratively train a global model from online task streams in dynamic real-world scenarios.
ACU: Analytic Continual Unlearning for Efficient and Exact Forgetting with Privacy Preservation
The development of artificial intelligence demands that models incrementally update knowledge by Continual Learning (CL) to adapt to open-world environments.
SegACIL: Solving the Stability-Plasticity Dilemma in Class-Incremental Semantic Segmentation
Extensive experiments on the Pascal VOC2012 dataset show that SegACIL achieves superior performance in the sequential, disjoint, and overlap settings, offering a robust solution to the challenges of class-incremental semantic segmentation.
TS-ACL: Closed-Form Solution for Time Series-oriented Continual Learning
To address these two challenges, we propose TS-ACL, which leverages a gradient-free closed-form solution to avoid the catastrophic forgetting problem inherent in gradient-based optimization methods while simultaneously learning global distributions to resolve intra-class variations.
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS.
