Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

Foundation models (FM) have demonstrated remarkable performance across a wide range
of tasks (especially in the fields of natural language processing and computer vision) …

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but
efficient synthetic training datasets from large ones. Existing DD methods based on gradient …

Despite the tremendous progress on data-free knowledge distillation (DFKD) based on
synthetic data generation, there are still limitations in diverse and efficient data synthesis. It …

Abstract Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the
fundamental idea of carrying out knowledge transfer from a Teacher neural network to a …

This paper focuses on an under-explored yet important problem: Federated Class-Continual
Learning (FCCL), where new classes are dynamically added in federated learning. Existing …

In the last decade, many deep learning models have been well trained and made a great
success in various fields of machine intelligence, especially for computer vision and natural …

This paper reveals a new appeal of the recently emerged large-kernel Convolutional Neural
Networks (ConvNets): as the teacher in Knowledge Distillation (KD) for small-kernel …

Abstract Data-free Knowledge Distillation (DFKD) has attracted attention recently thanks to
its appealing capability of transferring knowledge from a teacher network to a student …

Abstract Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue
involving the dynamic addition of new classes in the context of federated learning. In this …