Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators

Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in multi-label learning is extremely expensive, but also many of the actual labels are corrupted or missing due to the automated or non-expert annotation techniques. Noisy...

Robust multi-label learning for weakly labeled data

Multi-label learning is one of the hot problems in the field of machine learning. The deep neural networks used to solve it could be quite complex and have a huge capacity. This enormous capacity, however, could also be a negative, as they tend to eventually overfit the undesirable features of the data. One such feature presented in the real...

Improving the robustness of decision trees in security-sensitive setting

Machine learning is used for security purposes, to differ between the benign and the malicious. Where decision trees can lead to understandable and explainable classifications, an adversary could manipulate the model input to evade detection, e.g. the malicious been classified as the benign. State-of-the-art techniques improve the robustness by...