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Found 3 papers, 1 papers with code
Dynamic Visual Semantic Sub-Embeddings and Fast Re-Ranking
In this work, we propose a Dynamic Visual Semantic Sub-Embeddings framework (DVSE) to reduce the information entropy.
Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks
On the algorithmic side, we derive a new algorithm for black-box targeted attacks based on our theoretical analysis, in which we additionally minimize the maximum model discrepancy(M3D) of the substitute models when training the generator to generate adversarial examples.
Neyman-Pearson Classification under High-Dimensional Settings
Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other.
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