Search Results for author:
Found 4 papers, 4 papers with code
Safe and Robust Watermark Injection with a Single OoD Image
Training a high-performance deep neural network requires large amounts of data and computational resources.
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access to the original training data used for training the teacher model.
Backdoor Defense for Data-Free Distillation with Poisoned Teachers
Data-free Knowledge Distillation
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL.
Federated Adversarial Debiasing for Fair and Transferable Representations
While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework.
