CIFAR-10
A commonly used image classification benchmark dataset containing 32x32 color images across 10 classes, with 6,000 images per class and 60,000 in total. It is widely used for evaluating machine learning models.
Timeline (last 90 days)
The paper tested GCD on CIFAR-10 and ImageNet, covering three architectures, ResNet-18, ResNet-50, and ViT-B/16, and evaluated four mainstream attack methods.
RASS achieves approximately 20% speedup on CIFAR-10 under matched computational budget conditions.
The paper evaluated RASS on four types of tasks: Gaussian mixture target distributions, FFHQ unconditional pixel-space generation, CIFAR-10 conditional generation, and latent-space diffusion based on Stable Diffusion 3.5.
The paper validates the method on two types of architectures—fully connected networks and Vision Transformers—using the MNIST and CIFAR-10 datasets.
This study is based on CIFAR-10, a relatively simple dataset, and the optimal rank may shift upward for more complex, higher-resolution tasks.
The study uses a DDPM-architecture U-Net as the backbone on the CIFAR-10 dataset, testing five different rank values: {2, 4, 8, 16, 32}.
CIFAR-10 is an image classification benchmark dataset released by Alex Krizhevsky and Geoffrey Hinton in 2009, containing 60,000 32×32 pixel color images across 10 categories, with 50,000 for training and 10,000 for testing.
All Facts (7)
The paper tested GCD on CIFAR-10 and ImageNet, covering three architectures, ResNet-18, ResNet-50, and ViT-B/16, and evaluated four mainstream attack methods.
50%UnverifiedRASS achieves approximately 20% speedup on CIFAR-10 under matched computational budget conditions.
50%UnverifiedThe paper evaluated RASS on four types of tasks: Gaussian mixture target distributions, FFHQ unconditional pixel-space generation, CIFAR-10 conditional generation, and latent-space diffusion based on Stable Diffusion 3.5.
50%UnverifiedThe paper validates the method on two types of architectures—fully connected networks and Vision Transformers—using the MNIST and CIFAR-10 datasets.
50%UnverifiedThis study is based on CIFAR-10, a relatively simple dataset, and the optimal rank may shift upward for more complex, higher-resolution tasks.
50%UnverifiedThe study uses a DDPM-architecture U-Net as the backbone on the CIFAR-10 dataset, testing five different rank values: {2, 4, 8, 16, 32}.
50%UnverifiedCIFAR-10 is an image classification benchmark dataset released by Alex Krizhevsky and Geoffrey Hinton in 2009, containing 60,000 32×32 pixel color images across 10 categories, with 50,000 for training and 10,000 for testing.
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