1.5k citations · 1.9k across the 19 of their papers we have counts for
5 papers · 1 filter
Multiple instance learning with graph neural networks
Ming Tu, Jing Huang, Xiaodong He +1
Multiple instance learning (MIL) aims to learn the mapping between a bag of instances and the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN)…
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
Pengcheng Li, Jinfeng Yi, Bowen Zhou +1
Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we improve the robustness of DNNs by utilizing techniques of…
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
Lan-Zhe Guo, Yu-Feng Li, Ming Li +3
Weakly supervised data are widespread and have attracted much attention. However, since label quality is often difficult to guarantee, sometimes the use of weakly supervised data w…
Few-shot Learning with Meta Metric Learners
Yu Cheng, Mo Yu, Xiaoxiao Guo +1
Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approache…
Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks
Cicero Nogueira dos Santos, Kahini Wadhawan, Bowen Zhou
We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning. Our DAN approach builds upon generative adversarial networks (GANs) an…