Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

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 …

Abstract Knowledge distillation aims to learn a lightweight student network from a pre-
trained teacher network. In practice, existing knowledge distillation methods are usually …

Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm
in which clients enable collaborative training without compromising private data. However …

The popularity of Machine Learning as a Service (MLaaS) has led to increased concerns
about Model Stealing Attacks (MSA), which aim to craft a clone model by querying MLaaS …

Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance
student models using only the pre-trained teacher network without original training data …

Data-free knowledge distillation (DFKD) is a promising approach for addressing issues
related to model compression, security privacy, and transmission restrictions. Although the …

Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm
in which clients enable collaborative training without compromising private data. However …

Abstract Knowledge Distillation (KD) aims to learn a compact student network using
knowledge from a large pre-trained teacher network, where both networks are trained on …

Generative foundation models like Stable Diffusion comprise a diverse spectrum of
knowledge in computer vision with the potential for transfer learning, eg, via generating data …