Timezone: »
Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and improve feature transferability in the multiple task-specific layers. This paper presents Multilinear Relationship Networks (MRN) that discover the task relationships based on novel tensor normal priors over parameter tensors of multiple task-specific layers in deep convolutional networks. By jointly learning transferable features and multilinear relationships of tasks and features, MRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer. Experiments show that MRN yields state-of-the-art results on three multi-task learning datasets.
Author Information
Mingsheng Long (Tsinghua University)
ZHANGJIE CAO (Stanford University)
Jianmin Wang (Tsinghua University)
Philip S Yu (UIC)
More from the Same Authors
-
2021 Poster: From Canonical Correlation Analysis to Self-supervised Graph Neural Networks »
Hengrui Zhang · Qitian Wu · Junchi Yan · David Wipf · Philip S Yu -
2021 Poster: Cycle Self-Training for Domain Adaptation »
Hong Liu · Jianmin Wang · Mingsheng Long -
2021 Poster: Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality »
Songyuan Zhang · ZHANGJIE CAO · Dorsa Sadigh · Yanan Sui -
2021 Poster: Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting »
Haixu Wu · Jiehui Xu · Jianmin Wang · Mingsheng Long -
2020 Poster: Co-Tuning for Transfer Learning »
Kaichao You · Zhi Kou · Mingsheng Long · Jianmin Wang -
2020 Poster: Transferable Calibration with Lower Bias and Variance in Domain Adaptation »
Ximei Wang · Mingsheng Long · Jianmin Wang · Michael Jordan -
2020 Poster: Stochastic Normalization »
Zhi Kou · Kaichao You · Mingsheng Long · Jianmin Wang -
2020 Poster: Learning to Adapt to Evolving Domains »
Hong Liu · Mingsheng Long · Jianmin Wang · Yu Wang -
2020 : Broad Learning: A New Perspective on Mining Big Data »
Philip S Yu -
2019 Poster: Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning »
Xinyang Chen · Sinan Wang · Bo Fu · Mingsheng Long · Jianmin Wang -
2019 Poster: Transferable Normalization: Towards Improving Transferability of Deep Neural Networks »
Ximei Wang · Ying Jin · Mingsheng Long · Jianmin Wang · Michael Jordan -
2018 Poster: Conditional Adversarial Domain Adaptation »
Mingsheng Long · ZHANGJIE CAO · Jianmin Wang · Michael Jordan -
2018 Poster: Generalized Zero-Shot Learning with Deep Calibration Network »
Shichen Liu · Mingsheng Long · Jianmin Wang · Michael Jordan -
2017 Poster: PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs »
Yunbo Wang · Mingsheng Long · Jianmin Wang · Zhifeng Gao · Philip S Yu -
2016 Poster: Unsupervised Domain Adaptation with Residual Transfer Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2015 Workshop: Transfer and Multi-Task Learning: Trends and New Perspectives »
Anastasia Pentina · Christoph Lampert · Sinno Jialin Pan · Mingsheng Long · Judy Hoffman · Baochen Sun · Kate Saenko