While neural network models have been successfully applied to domains that
require substantial generalisation skills, recent studies have implied that
they struggle when solving the task they are trained on requires inferring its
underlying compositional structure. In this paper, we introduce Attentive
Guidance, a mechanism to direct a sequence to sequence model equipped with
attention to find more compositional solutions. We test it on two tasks,
devised precisely to assess the compositional capabilities of neural models,
and we show that vanilla sequence to sequence models with attention overfit the
training distribution, while the guided versions come up with compositional
solutions that fit the training and testing distributions almost equally well.
Moreover, the learned solutions generalise even in cases where the training and
testing distributions strongly diverge. In this way, we demonstrate that
sequence to sequence models are capable of finding compositional solutions
without requiring extra components. These results helps to disentangle the
causes for the lack of systematic compositionality in neural networks, which
can in turn fuel future work.

介绍了 Attentive Guidance，这是一种机制，可以指导序列到序列模型配备关注机制，以找到更多的组合解决方案。通过测试，证明序列到序列模型具备在不需要额外组件的情况下找到组成性解决方案的能力。

通过专注的指导进行复合学习

Learning compositionally through attentive guidance

Grasping skill is a major ability that a wide number of real-life
applications require for robotisation. State-of-the-art robotic grasping
methods perform prediction of object grasp locations based on deep neural
networks. However, such networks require huge amount of labeled data for
training making this approach often impracticable in robotics. In this paper,
we propose a method to generate a large scale synthetic dataset with ground
truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on
a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D
images and annotations of successful grasping positions based on grasp attempts
performed in a simulated environment. We carried out experiments using an
off-the-shelf CNN, with three different evaluation metrics, including real
grasping robot trials. The results show that Jacquard enables much better
generalization skills than a human labeled dataset thanks to its diversity of
objects and grasping positions. For the purpose of reproducible research in
robotics, we are releasing along with the Jacquard dataset a web interface for
researchers to evaluate the successfulness of their grasping position
detections using our dataset.

本文提出了一种生成大规模合成数据集的方法 --Jacquard 抓取数据集，并在实验中使用了一个 CNN 模型进行了测试，结果表明该数据集比人工标记的数据集具有更好的性能表现和泛化能力。同时，该文还提供一个网页界面，帮助其他研究人员通过该数据集测试物体的抓取性能。