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Found 14 papers, 8 papers with code
Adversarial Examples for Semantic Segmentation and Object Detection
Our observation is that both segmentation and detection are based on classifying multiple targets on an image (e. g., the basic target is a pixel or a receptive field in segmentation, and an object proposal in detection), which inspires us to optimize a loss function over a set of pixels/proposals for generating adversarial perturbations.
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts.
Mitigating Adversarial Effects Through Randomization
Convolutional neural networks have demonstrated high accuracy on various tasks in recent years.
Satellite-relayed intercontinental quantum network
This was on the one hand the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China.
Quantum Physics
Improving Transferability of Adversarial Examples with Input Diversity
We hope that our proposed attack strategy can serve as a strong benchmark baseline for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in the future.
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
Zero-Shot Transfer VQA Dataset
Therefore, toaccelerate this research, we propose a newzero-shot transfer VQA(ZST-VQA)dataset by reorganizing the existing VQA v1. 0 dataset in the way that duringtraining, some words appear only in one module (i. e. questions) but not in theother (i. e. answers).
Towards Adversarially Robust Object Detection
Object detection is an important vision task and has emerged as an indispensable component in many vision system, rendering its robustness as an increasingly important performance factor for practical applications.
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks
In this paper, we propose a joint adversarial training method that incorporates both spatial transformation-based and pixel-value based attacks for improving model robustness.
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
We introduce a feature scattering-based adversarial training approach for improving model robustness against adversarial attacks.
Compositional Generalization for Primitive Substitutions
Compositional generalization is a basic mechanism in human language learning, but current neural networks lack such ability.
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they remain adversarial even against other models.
Machine Learning on Volatile Instances
Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes.
Band-limited Soft Actor Critic Model
Soft Actor Critic (SAC) algorithms show remarkable performance in complex simulated environments.
