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Found 4 papers, 2 papers with code
Distilled Datamodel with Reverse Gradient Matching
To investigate the impact of changes in training data on a pre-trained model, a common approach is leave-one-out retraining.
Mutual-modality Adversarial Attack with Semantic Perturbation
Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
Dataset Distillation: A Comprehensive Review
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, unfortunately, significantly increases the burden on storage and transmission and further gives rise to a cumbersome model training process.
Federated Selective Aggregation for Knowledge Amalgamation
The goal of FedSA is to train a student model for a new task with the help of several decentralized teachers, whose pre-training tasks and data are different and agnostic.
