39 citations · 92 across the 12 of their papers we have counts for
5 papers · 1 filter
Multi-Task Adversarial Attack
Pengxin Guo, Yuancheng Xu, Baijiong Lin +1
Deep neural networks have achieved impressive performance in various areas, but they are shown to be vulnerable to adversarial attacks. Previous works on adversarial attacks mainly…
Effective, Efficient and Robust Neural Architecture Search
Zhixiong Yue, Baijiong Lin, Xiaonan Huang +1
Recent advances in adversarial attacks show the vulnerability of deep neural networks searched by Neural Architecture Search (NAS). Although NAS methods can find network architectu…
Distant Transfer Learning via Deep Random Walk
Qiao Xiao, Yu Zhang
Transfer learning, which is to improve the learning performance in the target domain by leveraging useful knowledge from the source domain, often requires that those two domains ar…
Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
Pengxin Guo, Chang Deng, Linjie Xu +2
Deep multi-task learning attracts much attention in recent years as it achieves good performance in many applications. Feature learning is important to deep multi-task learning for…
A Simple General Approach to Balance Task Difficulty in Multi-Task Learning
Sicong Liang, Yu Zhang
In multi-task learning, difficulty levels of different tasks are varying. There are many works to handle this situation and we classify them into five categories, including the dir…