19 datasets found

Groups: Robustness

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  • MNIST-C

    The MNIST-C dataset is a comprehensive suite of different corruptions applied to the MNIST dataset, for benchmarking out-of-distribution robustness in computer vision.

  • Color MNIST

    The Color MNIST dataset is used for visual concepts from the Color MNIST dataset of Seo et al. (2017).

  • Robust Gaussian Filter

    The dataset used in the paper is a simulation of a system with fat-tailed measurement described in Example 4.1.

  • nvBench-Rob(nlq,schema)

    The nvBench-Rob(nlq,schema) dataset is a testing set from nvBench-Rob, containing both NLQ variants and data schema variants, specifically designed to test the robustness of...

  • nvBench-Robschema

    The nvBench-Robschema dataset is a testing set from nvBench-Rob, containing only data schema variants, specifically designed to test the robustness of models against data schema...

  • nvBench-Robnlq

    The nvBench-Robnlq dataset is a testing set from nvBench-Rob, containing only NLQ variants, specifically designed to test the robustness of models against NLQ variants.

  • nvBench-Rob

    The nvBench-Rob dataset is a comprehensive robustness evaluation dataset for text-to-vis models, containing diverse lexical and phrasal variations based on the original...

  • An Efficient Solution to s-Rectangular Robust Markov Decision Processes

    The dataset is used for s-rectangular Lp robust Markov Decision Processes (MDPs) with a time complexity comparable to standard (non-robust) MDPs.

  • Noisy Recurrent Neural Networks

    The dataset is a class of noisy recurrent neural networks with w (unbounded) weights for classification of sequences of length T, where independent noise distributed according...

  • KITTI-C

    The KITTI-C dataset is a robustness benchmark for outdoor monocular depth estimation under data corruptions.

  • Informed Non-convex Robust Principal Component Analysis with Features

    The dataset used in this paper is a low-rank matrix M, which can be decomposed into a low-rank component L∗ and a sparse error matrix S∗. The authors use this dataset to test...

  • Multimodal Robustness Benchmark

    The MMR benchmark is designed to evaluate MLLMs' comprehension of visual content and robustness against misleading questions, ensuring models truly leverage multimodal inputs...

  • CIFAR-10-C and CIFAR-100-C

    CIFAR-10-C and CIFAR-100-C are robustness benchmarks consisting of 19 corruptions types with five levels of severities.

  • CMNIST

    Dataset bias is a significant problem in training fair classifiers. When attributes unrelated to classification exhibit strong biases towards certain classes, classifiers...

  • LEARNING PERTURBATION SETS FOR ROBUST MACHINE LEARNING

    A general framework for learning perturbation sets from data when the perturbation cannot be mathematically-defined.

  • LAV Dataset

    The LAV dataset is used to evaluate the robustness of the proposed Penalty-based Imitation Learning with Cross Semantics Generation approach.

  • Imbalanced Gradients

    The Imbalanced Gradients dataset is a benchmark for evaluating the robustness of deep neural networks.

  • 3DeformRS: Certifying Spatial Deformations on Point Clouds

    3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these...

  • ImageNet-C

    The dataset used in the paper is the ImageNet-C dataset, which is a dataset of images corrupted with various types of noise and occlusions.