Search Results for author:
Found 3 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.
HpGAN: Sequence Search with Generative Adversarial Networks
Sequences play an important role in many engineering applications and systems.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
