Abstract Knowledge distillation aims to compress a powerful yet cumbersome teacher model
into a lightweight student model without much sacrifice of performance. For this purpose …

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 …

Data-free knowledge distillation (DFKD) has recently been attracting increasing attention
from research communities, attributed to its capability to compress a model only using …

Abstract Knowledge distillation is an effective method for training small and efficient deep
learning models. However the efficacy of a single method can degenerate when transferring …

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 …

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

Data-free knowledge distillation is able to utilize the knowledge learned by a large teacher
network to augment the training of a smaller student network without accessing the original …

Data-free knowledge distillation (DFKD) improves the student model (S) by mimicking the
class probability from a pre-trained teacher model (T) without training data. Under such …

Rising concerns about privacy and anonymity preservation of deep learning models have
facilitated research in data-free learning. Primarily based on data-free knowledge distillation …

Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional
performance across numerous applications. However, Model Inversion (MI) attacks, which …