FAUN recovers poisoned federated learning models via adversarial updates on a proxy dataset, matching retraining performance with far fewer rounds and near-zero attack success.
Deep residual learning for image recognition,
3 Pith papers cite this work. Polarity classification is still indexing.
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CS3H introduces a collision-resistant single-pass framework using normalized Hamming distance loss and attention to improve separation in unsupervised fine-grained image hashing.
DRN uses residual distilling blocks (RDB) and groups (RDG) to achieve a better performance to model size trade-off in single image super-resolution than existing methods.
citing papers explorer
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Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
FAUN recovers poisoned federated learning models via adversarial updates on a proxy dataset, matching retraining performance with far fewer rounds and near-zero attack success.
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Collision-Resistant Single-Pass Method for Unsupervised Fine-Grained Image Hashing
CS3H introduces a collision-resistant single-pass framework using normalized Hamming distance loss and attention to improve separation in unsupervised fine-grained image hashing.
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Distilling with Residual Network for Single Image Super Resolution
DRN uses residual distilling blocks (RDB) and groups (RDG) to achieve a better performance to model size trade-off in single image super-resolution than existing methods.