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Found 2 papers, 2 papers with code
Data-driven Knowledge Fusion for Deep Multi-instance Learning
DKMIL adopts a completely different idea from existing deep MIL methods by analyzing the decision-making of key samples in the data set (referred to as the data-driven) and using the knowledge fusion module designed to extract valuable information from these samples to assist the model's training.
Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial Perturbations
Through simulations, we have also shown the effectiveness of the proposed algorithms to fool the state-of-the-art (SOTA) MIL methods.
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