摘要
In this paper, we present an effective dataset compression approach based on the matrix product states (short as MPS) and knowledge distillation. MPS can decompose image samples into a sequential product of tensors to achieve task-agnostic image compression by preserving the low-rank information of the images. Based on this property, we use multiple MPS to represent the image datasets samples. Meanwhile, we also designed a task-related component based on knowledge distillation to enhance the generality of the compressed dataset.