Learning Multiple Tasks with Multilinear Relationship Networks
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 negativetransfer 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.
This paper proposes a solution to a very interesting problem, namely multi-task learning. The authors tackles learning task similarities. It is known to be very hard problem and it has been ignored in many problems that were proposed to solve multi-task learning. The authors proposes a Bayesian method by using tensor normal priors. The paper is well written and well connected to literature. The idea is quite novel. The authors supply a set of convincing experiments and to support their method. 
Although I am quite positive about paper, I would like to get information to the following questions:
1) Although experiments are convincing, I would like to see the standard deviations (stds)for experiments? Since the dataset sizes are small then reporting stds are quite important.
2) The authors uses AlexNet as a baseline. However, AlexNet architecture is not anymore the state of the art architecture. It would be great if authors uses GoogleNet or something similar and show the improvement of their algorithm.