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Soft-Label Dataset Distillation and Text Dataset Distillation
Dataset distillation is a method for reducing dataset sizes by learning a
small number of synthetic samples containing all the information of a large
dataset. This has several benefits like speeding up model training, reducing
energy consumption, and reducing required storage space. Currently, each
synthetic sample is assigned a single `hard' label, and also, dataset
distillation can currently only be used with image data.
We propose to simultaneously distill both images and their labels, thus
assigning each synthetic sample a `soft' label (a distribution of labels). Our
algorithm increases accuracy by 2-4% over the original algorithm for several
image classification tasks. Using `soft' labels also enables distilled datasets
to consist of fewer samples than there are classes as each sample can encode
information for multiple classes. For example, training a LeNet model with 10
distilled images (one per class) results in over 96% accuracy on MNIST, and
almost 92% accuracy when trained on just 5 distilled images.
We also extend the dataset distillation algorithm to distill sequential
datasets including texts. We demonstrate that text distillation outperforms
other methods across multiple datasets. For example, models attain almost their
original accuracy on the IMDB sentiment analysis task using just 20 distilled
sentences.
Our code can be found at
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