Search Results for author:
Found 14 papers, 3 papers with code
Identifying Important Group of Pixels using Interactions
To better understand the behavior of image classifiers, it is useful to visualize the contribution of individual pixels to the model prediction.
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition
Using Fourier analysis, we explore the robustness and vulnerability of graph convolutional neural networks (GCNs) for skeleton-based action recognition.
Exploiting Frequency Spectrum of Adversarial Images for General Robustness
In recent years, there has been growing concern over the vulnerability of convolutional neural networks (CNNs) to image perturbations.
Improving Zero-Shot Action Recognition using Human Instruction with Text Description
Furthermore, the proposed model can also be combined with other models to improve its accuracy.
Spatiotemporal forecasting of vertical track alignment with exogenous factors
In this study, a method is proposed to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations.
Game-Theoretic Understanding of Misclassification
This paper analyzes various types of image misclassification from a game-theoretic view.
Adversarial Body Shape Search for Legged Robots
We propose an evolutionary computation method for an adversarial attack on the length and thickness of parts of legged robots by deep reinforcement learning.
Adversarial joint attacks on legged robots
In experiments with the quadruped robot Ant-v2 and the bipedal robot Humanoid-v2, in OpenAI Gym environments, we find that differential evolution can efficiently find the strongest torque perturbations among the three methods.
Adversarial amplitude swap towards robust image classifiers
Extensive experiments revealed that the images generated by combining the amplitude spectrum of adversarial images and the phase spectrum of clean images accommodates moderate and general perturbations, and training with these images equips a CNN classifier with more general robustness, performing well under both common corruptions and adversarial perturbations.
Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control
This study is aimed at addressing the problem of fault tolerance of quadruped robots to actuator failure, which is critical for robots operating in remote or extreme environments.
Conditional MoCoGAN for Zero-Shot Video Generation
To realize this objective, we base our model on the motion and content decomposed GAN and conditional GAN for image generation.
Adversarial Bone Length Attack on Action Recognition
Specifically, we restrict the perturbations to the lengths of the skeleton's bones, which allows an adversary to manipulate only approximately 30 effective dimensions.
Adversarially Trained Object Detector for Unsupervised Domain Adaptation
In addition, we propose a method that combines adversarial training and feature alignment to ensure the improved alignment of robust features with the target domain.
