Improving Dataset Distillation
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Dataset distillation is a method for reducing dataset sizes: the goal is to learn a small number of synthetic samples containing all the information of a large dataset. This has several benefits: speeding up model training in deep learning, reducing energy consumption, and reducing required storage space. Currently, each synthetic sample is assigned a single `hard' label, which limits the accuracies that models trained on distilled datasets can achieve. Also, currently dataset distillation can only be used with image data. We propose to simultaneously distill both images and their labels, and thus to assign each synthetic sample a `soft' label (a distribution of labels) rather than a single `hard' label. Our improved algorithm increases accuracy by 2-4 classification tasks. For example, training a LeNet model with just 10 distilled images (one per class) results in over 96 data. Using `soft' labels also enables distilled datasets to consist of fewer samples than there are classes as each sample can encode information for more than one class. For example, we show that LeNet achieves almost 92 MNIST after being trained on just 5 distilled images. We also propose an extension of the dataset distillation algorithm that allows it to distill sequential datasets including texts. We demonstrate that text distillation outperforms other methods across multiple datasets. For example, we are able to train models to almost their original accuracy on the IMDB sentiment analysis task using just 20 distilled sentences.
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