Title: Deep Asymmetric Multi-task Feature Learning,
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.
Specifically, we introduce an asymmetric autoencoder term that allows reliable
predictors for the easy tasks to have high contribution to the feature learning
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 knowledge from the reliable predictors via the
shared latent features. Such asymmetric knowledge transfer through shared
features is also more scalable and efficient than inter-task asymmetric
transfer. We validate our Deep-AMTFL model on multiple benchmark datasets for
multitask learning and image classification, on which it significantly
outperforms existing symmetric and asymmetric multitask learning models, by
effectively preventing negative transfer in deep feature learning.