text: The collaborative block that authors propose is a generalized module that can be inserted in deep architectures for better multi-task learning. The problem is relevant as we are pushing deep networks to learn representation for multiple tasks. The proposed method while simple is novel. The few places where the paper needs improvement are: 1. The authors should test their collaborative block on multiple tasks where the tasks are less related. Ex: Scene and object classification. The current datasets where the model is evaluated is limited to Faces which is a constrained setting. It would be great if Authors provide more experiments beyond Faces to test the universality of the proposed approach. 2. The Face datasets are rather small. I wonder if the accuracy improvements hold on larger datasets and if authors can comment on any large scale experiments they have done using the proposed architecture. In it's current form I would say the experiment section and large scale experiments are two places where the paper falls short.