Timezone: »
The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks. Frequent violation of the assumption in applications with either insufficient tasks or a very narrow meta-training task distribution leads to memorization or learner overfitting. Recent solutions have pursued augmentation of meta-training tasks, while it is still an open question to generate both correct and sufficiently imaginary tasks. In this paper, we seek an approach that up-samples meta-training tasks from the task representation via a task up-sampling network. Besides, the resulting approach named Adversarial Task Up-sampling (ATU) suffices to generate tasks that can maximally contribute to the latest meta-learner by maximizing an adversarial loss. On few-shot sine regression and image classification datasets, we empirically validate the marked improvement of ATU over state-of-the-art task augmentation strategies in the meta-testing performance and also the quality of up-sampled tasks.
Author Information
Yichen WU (City University of Hong Kong)
Long-Kai Huang (Nanyang Technological University)
Ying Wei (City University of Hong Kong)
More from the Same Authors
-
2023 Poster: Secure Out-of-Distribution Task Generalization with Energy-Based Models »
Shengzhuang Chen · Long-Kai Huang · Jonathan Richard Schwarz · Yilun Du · Ying Wei -
2023 Poster: Retaining Beneficial Information from Detrimental Data for Neural Network Repair »
Long-Kai Huang · Peilin Zhao · Junzhou Huang · Sinno Pan -
2023 Poster: Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation Framework »
Weiduo Liao · Ying Wei · Mingchen Jiang · Qingfu Zhang · Hisao Ishibuchi -
2022 Spotlight: Adversarial Task Up-sampling for Meta-learning »
Yichen WU · Long-Kai Huang · Ying Wei -
2022 Spotlight: Lightning Talks 1B-3 »
Chaofei Wang · Qixun Wang · Jing Xu · Long-Kai Huang · Xi Weng · Fei Ye · Harsh Rangwani · shrinivas ramasubramanian · Yifei Wang · Qisen Yang · Xu Luo · Lei Huang · Adrian G. Bors · Ying Wei · Xinglin Pan · Sho Takemori · Hong Zhu · Rui Huang · Lei Zhao · Yisen Wang · Kato Takashi · Shiji Song · Yanan Li · Rao Anwer · Yuhei Umeda · Salman Khan · Gao Huang · Wenjie Pei · Fahad Shahbaz Khan · Venkatesh Babu R · Zenglin Xu -
2022 Spotlight: Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization »
Long-Kai Huang · Ying Wei -
2022 Poster: Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization »
Long-Kai Huang · Ying Wei -
2022 Poster: GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy »
Yemin Yu · Ying Wei · Kun Kuang · Zhengxing Huang · Huaxiu Yao · Fei Wu -
2021 Poster: Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery »
Huaxiu Yao · Ying Wei · Long-Kai Huang · Ding Xue · Junzhou Huang · Zhenhui (Jessie) Li -
2021 Poster: Meta-learning with an Adaptive Task Scheduler »
Huaxiu Yao · Yu Wang · Ying Wei · Peilin Zhao · Mehrdad Mahdavi · Defu Lian · Chelsea Finn -
2020 Poster: Self-Supervised Graph Transformer on Large-Scale Molecular Data »
Yu Rong · Yatao Bian · Tingyang Xu · Weiyang Xie · Ying Wei · Wenbing Huang · Junzhou Huang