76 citations · 76 across the 4 of their papers we have counts for
4 papers
Autoencoder-Based Hybrid Replay for Class-Incremental Learning
Milad Khademi Nori, Il-Min Kim, Guanghui Wang
In class-incremental learning (CIL), effective incremental learning strategies are essential to mitigate task confusion and catastrophic forgetting, especially as the number of tas…
Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting
Milad Khademi Nori, Il-Min Kim, Guanghui Wang
Federated Class-Incremental Learning (FCIL) refers to a scenario where a dynamically changing number of clients collaboratively learn an ever-increasing number of incoming tasks. F…
Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings
Milad Khademi Nori, Il-Min Kim
In class-incremental learning (class-IL), models must classify all previously seen classes at test time without task-IDs, leading to task confusion. Despite being a key challenge,…
Fast Federated Learning by Balancing Communication Trade-Offs
Milad Khademi Nori, Sangseok Yun, Il-Min Kim
Federated Learning (FL) has recently received a lot of attention for large-scale privacy-preserving machine learning. However, high communication overheads due to frequent gradient…