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Found 6 papers, 0 papers with code
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks
Leveraging this, we introduce DISBELIEVE, a local model poisoning attack that creates malicious parameters or gradients such that their distance to benign clients' parameters or gradients is low respectively but at the same time their adverse effect on the global model's performance is high.
Improved Techniques for the Conditional Generative Augmentation of Clinical Audio Data
In contrast to previously proposed fully convolutional models, the proposed model implements residual Squeeze and Excitation modules in the generator architecture.
Synthetic Data in Human Analysis: A Survey
This survey is intended for researchers and practitioners in the field of human analysis.
DILF-EN framework for Class-Incremental Learning
We apply our proposed approach to state-of-the-art class-incremental learning methods and empirically show that our framework significantly improves the performance of these methods.
Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning
In order to save the human effort in generating annotations required by state-of-the-art, we propose a fingerprint roi segmentation model which aligns the features of fingerprint images derived from the unseen sensor such that they are similar to the ones obtained from the fingerprints whose ground truth roi masks are available for training.
Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints
The effectiveness of fingerprint-based authentication systems on good quality fingerprints is established long back.
