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Found 3 papers, 2 papers with code
Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples
Over the last couple of years few-shot learning (FSL) has attracted great attention towards minimizing the dependency on labeled training examples.
Key Protected Classification for Collaborative Learning
Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners.
Key Protected Classification for GAN Attack Resilient Collaborative Learning
To the best of our knowledge, the proposed approach is the first collaborative learning formulation that effectively tackles an active adversary, and, unlike model corruption or differential privacy formulations, our approach does not inherently feature a trade-off between model accuracy and data privacy.
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