CVPR 2024 论文列表

Data privacy is of great concern in cloud machine-learning service platforms when sensitive data are exposed to service providers. While private computing environments (e.g. secure enclaves) and cryptographic approaches (e.g. homomorphic encryption) provide strong privacy protection their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance we propose Delta a new private training and inference framework with comparable model performance as non-private centralized training. Delta features two asymmetric data flows: the main information-sensitive flow and the residual flow. The main part flows into a small model while the residuals are offloaded to a large model. Specifically Delta embeds the information-sensitive representations into a low-dimensional space while pushing the information-insensitive part into high-dimension residuals. To ensure privacy protection the low-dimensional information-sensitive part is secured and fed to a small model in a private environment. On the other hand the residual part is sent to fast cloud GPUs and processed by a large model. To further enhance privacy and reduce the communication cost Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform. We theoretically show that Delta guarantees differential privacy in the public environment and greatly reduces the complexity in the private environment. We conduct empirical analyses on CIFAR-10 CIFAR-100 and ImageNet datasets and ResNet-18 and ResNet-34 showing that Delta achieves strong privacy protection fast training and inference without significantly compromising the model utility.

Convolutional neural networks benefit from translation equivariance achieving tremendous success. Equivariant networks further extend this property to other transformation groups. However most existing methods require discretization or sampling of groups leading to increased model sizes for larger groups such as the affine group. In this paper we build affine equivariant networks based on differential invariants from the viewpoint of symmetric PDEs without discretizing or sampling the group. To address the division-by-zero issue arising from fractional differential invariants of the affine group we construct a new kind of affine invariants by normalizing polynomial relative differential invariants to replace classical differential invariants. For further flexibility we design an equivariant layer which can be directly integrated into convolutional networks of various architectures. Moreover our framework for the affine group is also applicable to its continuous subgroups. We implement equivariant networks for the scale group the rotation-scale group and the affine group. Numerical experiments demonstrate the outstanding performance of our framework across classification tasks involving transformations of these groups. Remarkably under the out-of-distribution setting our model achieves a 3.37% improvement in accuracy over the main counterpart affConv on the affNIST dataset.