254 citations · 304 across the 20 of their papers we have counts for
6 papers · 1 filter
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
Krishnateja Killamsetty, Changbin Li, Chen Zhao +2
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal. Hence, i…
End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining
Song-Lu Chen, Shu Tian, Jia-Wei Ma +4
License plate detection is the first and essential step of the license plate recognition system and is still challenging in real applications, such as on-road scenarios. In particu…
How Out-of-Distribution Data Hurts Semi-Supervised Learning
Xujiang Zhao, Killamsetty Krishnateja, Rishabh Iyer +1
Recent semi-supervised learning algorithms have demonstrated greater success with higher overall performance due to better-unlabeled data representations. Nonetheless, recent resea…
Fair Meta-Learning For Few-Shot Classification
Chen Zhao, Changbin Li, Jincheng Li +1
Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems. A machine le…
Unfairness Discovery and Prevention For Few-Shot Regression
Chen Zhao, Feng Chen
We study fairness in supervised few-shot meta-learning models that are sensitive to discrimination (or bias) in historical data. A machine learning model trained based on biased da…
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an…