CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

no code implementations • 28 Oct 2021 • Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang

In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i. e., the loss changes with respect to model weights and node features, respectively.

A HYPOTHESIS FOR THE COGNITIVE DIFFICULTY OF IMAGES

no code implementations • 29 Sep 2021 • Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Xin Jin, Quanshi Zhang

This paper proposes a hypothesis to analyze the underlying reason for the cognitive difficulty of an image from two perspectives, i. e. a cognitive image usually makes a DNN strongly activated by cognitive concepts; discarding massive non-cognitive concepts may also help the DNN focus on cognitive concepts.

A Hypothesis for the Aesthetic Appreciation in Neural Networks

no code implementations • 31 Jul 2021 • Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Quanshi Zhang

This paper proposes a hypothesis for the aesthetic appreciation that aesthetic images make a neural network strengthen salient concepts and discard inessential concepts.

Towards a Unified Evaluation of Explanation Methods without Ground Truth

no code implementations • 20 Nov 2019 • Hao Zhang, Jiayi Chen, Haotian Xue, Quanshi Zhang

This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges.