Boosting Dataset Distillation with the Assistance of Crucial Samples for Visual Learning

In recent years, massive datasets have significantly driven the advancement of visual learning such as multi-modal large model at the expense of high computational costs and extensive storage requirements. Dataset distillation (DD) aims to address this challenge by learning a small synthetic dataset such that a model trained on it can achieve a test performance comparable to that of the model trained on the original dataset. This task can be formulated as a bi-level learning problem where the outer loop optimizes the learned dataset and the inner loop updates the model parameters based on the distilled data. Different from previous studies that focus primarily on optimizing the inner loop in this bi-level problem, we delve into the task of dataset distillation from the perspective of sample cruciality. We find that discarding easy samples and keeping the hard ones that are difficult to be represented by the learned synthetic samples in the outer loop can be beneficial for DD. Motivated by this observation, we further develop an Infinite Semantic Augmentation (ISA) based dataset distillation algorithm, which discards some easier samples and implicitly enriches harder ones in the semantic space through continuous interpolation between two target feature vectors. Through detailed mathematical derivation, the joint contribution to the training loss of all interpolated feature points is formed into an analytical closed-form solution of an integral that can be optimized with almost no extra computational cost. Experimental results on several benchmark datasets demonstrate the effectiveness of our approach in reducing the dataset size while preserving the accuracy of the model. Furthermore, we show that high-quality distilled data can also benefit downstream applications, such as continual learning and membership inference defense. The code is available at https://github.com/fiona-lxd/ISA.git.