Graph Neural Network (GNN) research has produced strategies to modify a
graph's edges using gradients from a trained GNN, with the goal of network
design. However, the factors which govern gradient-based editing are
understudied, obscuring why edges are chosen and if edits are grounded in an
edge's importance. Thus, we begin by analyzing the gradient computation in
previous works, elucidating the factors that influence edits and highlighting
the potential over-reliance on structural properties. Specifically, we find
that edges can achieve high gradients due to structural biases, rather than
importance, leading to erroneous edits when the factors are unrelated to the
design task. To improve editing, we propose ORE, an iterative editing method
that (a) edits the highest scoring edges and (b) re-embeds the edited graph to
refresh gradients, leading to less biased edge choices. We empirically study
ORE through a set of proposed design tasks, each with an external validation
method, demonstrating that ORE improves upon previous methods by up to 50%.

图神经网络（GNN）研究提出了使用训练后的 GNN 梯度来修改图的边缘以实现网络设计的策略，然而，调节基于梯度的编辑的因素尚未得到充分研究，模糊了为何选择边缘以及编辑是否基于边缘的重要性。因此，我们首先分析了先前工作中的梯度计算，阐明了影响编辑的因素并突出了对结构属性的过度依赖。具体来说，我们发现边缘之所以能够获得高梯度是由于结构偏倚而非重要性，这会导致与设计任务无关的错误编辑。为了改善编辑过程，我们提出了 ORE，一种迭代编辑方法，通过（a）编辑得分最高的边缘和（b）重新嵌入编辑后的图来刷新梯度，从而减少了边缘选择的偏见。我们通过一系列提出的设计任务进行了与外部验证方法相结合的 ORE 的实证研究，表明 ORE 的性能比先前方法提高了 50%。

通过图神经网络进行网络设计：识别挑战和提高性能

Network Design through Graph Neural Networks: Identifying Challenges and  Improving Performance

Humans have a natural instinct to identify unknown object instances in their
environments. The intrinsic curiosity about these unknown instances aids in
learning about them, when the corresponding knowledge is eventually available.
This motivates us to propose a novel computer vision problem called: `Open
World Object Detection', where a model is tasked to: 1) identify objects that
have not been introduced to it as `unknown', without explicit supervision to do
so, and 2) incrementally learn these identified unknown categories without
forgetting previously learned classes, when the corresponding labels are
progressively received. We formulate the problem, introduce a strong evaluation
protocol and provide a novel solution, which we call ORE: Open World Object
Detector, based on contrastive clustering and energy based unknown
identification. Our experimental evaluation and ablation studies analyze the
efficacy of ORE in achieving Open World objectives. As an interesting
by-product, we find that identifying and characterizing unknown instances helps
to reduce confusion in an incremental object detection setting, where we
achieve state-of-the-art performance, with no extra methodological effort. We
hope that our work will attract further research into this newly identified,
yet crucial research direction.

本文介绍了一个新的计算机视觉问题：“开放领域物体检测”，其中模型的任务是在未经明确监督的情况下识别未引入的对象，并在逐步接收相应标签时增量学习这些已识别的未知类别。阐述了该问题，并提供了一种名为 ORE 的解决方案，基于对比聚类和基于能源的未知物体识别。我们的实验评估和消融研究分析了 ORE 在实现开放世界目标方面的功效。同时，我们发现确定和表征未知实例有助于减少增量对象检测设置中的混淆，我们在没有额外的方法论努力下实现了最先进的性能。希望我们的工作能吸引更多关于此全新但至关重要的研究方向的研究。