Lifelong-CIFAR10, Lifelong-ImageNet

Lifelong-CIFAR10和Lifelong-ImageNet是两个大型的机器学习基准数据集，分别由1.69百万和1.98百万的测试样本组成。这些数据集由图宾根人工智能中心和图宾根大学创建，旨在通过持续增加样本量来评估模型性能，同时减少模型对特定数据集特征的过拟合。Lifelong-CIFAR10结合了多种CIFAR10变体，包括不同颜色和域的样本，以及通过扩散模型生成的合成样本。Lifelong-ImageNet则从ImageNet及其变体中提取样本，包含43个独特的域，旨在增加样本的多样性。这些数据集的应用领域包括视觉任务的模型性能评估，特别是在解决模型过拟合和提高泛化能力方面。

Lifelong-CIFAR10 and Lifelong-ImageNet are two large-scale machine learning benchmark datasets, consisting of 1.69 million and 1.98 million test samples respectively. These datasets were developed by the Tübingen AI Center and the University of Tübingen, aiming to evaluate model performance by continuously increasing the sample size while reducing the model's overfitting to specific dataset features. Lifelong-CIFAR10 incorporates multiple variants of CIFAR10, including samples with different colors and domains, as well as synthetic samples generated by diffusion models. Lifelong-ImageNet, on the other hand, extracts samples from ImageNet and its variants, covers 43 distinct domains, and aims to enhance sample diversity. The application fields of these datasets include model performance evaluation for visual tasks, particularly for mitigating model overfitting and improving generalization ability.