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Found 5 papers, 2 papers with code
Attacking Point Cloud Segmentation with Color-only Perturbation
Recent research efforts on 3D point-cloud semantic segmentation have achieved outstanding performance by adopting deep CNN (convolutional neural networks) and GCN (graph convolutional networks).
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption.
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms
Existing approximation algorithms, although achieving great improvement over the exact algorithm, relies on retraining models for multiple times, thus remaining limited when applied to larger-scale learning tasks and real-world datasets.
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.
Adversarial Attack Generation Empowered by Min-Max Optimization
In this paper, we show how a general framework of min-max optimization over multiple domains can be leveraged to advance the design of different types of adversarial attacks.
