  One of the main motivations of MTL is to develop neural networks capable of
inferring multiple tasks simultaneously. While countless methods have been
proposed in the past decade investigating robust model architectures and
efficient training algorithms, there is still lack of understanding of these
methods when applied on smaller feature extraction backbones, the
generalizability of the commonly used fast approximation technique of replacing
parameter-level gradients with feature level gradients, and lack of
comprehensive understanding of MTL challenges and how one can efficiently and
effectively identify the challenges. In this paper, we focus on the
aforementioned efficiency aspects of existing MTL methods. We first carry out
large-scale experiments of the methods with smaller backbones and on a the
MetaGraspNet dataset as a new test ground. We also compare the existing methods
with and without using the fast gradient surrogate and empirically study the
generalizability of this technique. Lastly, we propose Feature Disentanglement
measure as a novel and efficient identifier of the challenges in MTL, and
propose Ranking Similarity score as an evaluation metric for different
identifiers to prove the faithfulness of our method.
