5 datasets found

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 are robustness benchmarks consisting of 19 corruptions types with five levels of severities.

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

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

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