Convolutional neural networks (CNNs) have become the most successful approach
in many vision-related domains. However, they are limited to domains where data
is abundant. Recent works have looked at multi-task learning (MTL) to mitigate
data scarcity by leveraging domain-specific information from related tasks. In
this paper, we present a novel soft-parameter sharing mechanism for CNNs in a
MTL setting, which we refer to as Deep Collaboration. We propose taking into
account the notion that task relevance depends on depth by using lateral
transformation blocs with skip connections. This allows extracting
task-specific features at various depth without sacrificing features relevant
to all tasks. We show that CNNs connected with our Deep Collaboration obtain
better accuracy on facial landmark detection with related tasks. We finally
verify that our approach effectively allows knowledge sharing by showing
depth-specific influence of tasks that we know are related.

本文提出利用多任务学习中的软参数共享机制，在卷积神经网络中加入侧向转换块，并使用跳接连接的方式提取各深度的任务特定特征，从而在面部关键点检测等相关任务中取得更好的精度和深度特定影响的知识共享效果。