Deep Asymmetric Multi-task Feature Learning
This addresses negative transfer in multitask learning for AI/ML practitioners, representing an incremental improvement over existing asymmetric methods.
The paper tackles the problem of negative transfer in multitask learning by proposing Deep Asymmetric Multitask Feature Learning (Deep-AMTFL), which uses an asymmetric autoencoder to prioritize reliable predictors from easy tasks while suppressing unreliable ones from difficult tasks, resulting in significantly outperforming existing models on benchmark datasets.
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.