%0 Conference Paper %1 Lee2018DeepAM %A Lee, Haebeom %A Yang, Eunho %A Hwang, Sung Ju %B ICML %D 2018 %K asymetric deep_learning multitask vision %T Deep Asymmetric Multi-task Feature Learning %X We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifi- cally, we introduce an asymmetric autoencoder term that allows reliable predictors for the easy tasks to have high contribution to the feature learn- ing while suppressing the influences of unreliable predictors for more difficult tasks. This allows the learning of less noisy representations, and enables unreliable predictors to exploit knowl- edge from the reliable predictors via the shared latent features. Such asymmetric knowledge trans- fer through shared features is also more scalable and efficient than inter-task asymmetric transfer. We validate our Deep-AMTFL model on multi- ple benchmark datasets for multitask learning and image classification, on which it significantly out- performs existing symmetric and asymmetric mul- titask learning models, by effectively preventing negative transfer in deep feature learning.

@inproceedings{Lee2018DeepAM, abstract = {We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifi- cally, we introduce an asymmetric autoencoder term that allows reliable predictors for the easy tasks to have high contribution to the feature learn- ing while suppressing the influences of unreliable predictors for more difficult tasks. This allows the learning of less noisy representations, and enables unreliable predictors to exploit knowl- edge from the reliable predictors via the shared latent features. Such asymmetric knowledge trans- fer through shared features is also more scalable and efficient than inter-task asymmetric transfer. We validate our Deep-AMTFL model on multi- ple benchmark datasets for multitask learning and image classification, on which it significantly out- performs existing symmetric and asymmetric mul- titask learning models, by effectively preventing negative transfer in deep feature learning.}, added-at = {2018-08-27T22:17:25.000+0200}, author = {Lee, Haebeom and Yang, Eunho and Hwang, Sung Ju}, biburl = {https://www.bibsonomy.org/bibtex/25810725dff1fb5959b0466f42ff89ec3/dallmann}, booktitle = {ICML}, description = {Deep Asymmetric Multi-task Feature Learning - Semantic Scholar}, interhash = {75c6477f695c4b32e28328bc85b31744}, intrahash = {5810725dff1fb5959b0466f42ff89ec3}, keywords = {asymetric deep_learning multitask vision}, timestamp = {2018-08-27T22:17:25.000+0200}, title = {Deep Asymmetric Multi-task Feature Learning}, year = 2018 }