Data Distillation: A Survey

• URL: http://arxiv.org/abs/2301.04272v2
• Date: Tue, 26 Sep 2023 04:43:31 GMT
• Title: Data Distillation: A Survey
• Authors: Noveen Sachdeva, Julian McAuley
• Abstract summary: Deep learning has led to the curation of a vast number of massive and multifarious datasets. Despite having close-to-human performance on individual tasks, training parameter-hungry models on large datasets poses multi-faceted problems. Data distillation approaches aim to synthesize terse data summaries, which can serve as effective drop-in replacements of the original dataset.
• Score: 32.718297871027865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets. Despite having close-to-human performance on individual tasks, training parameter-hungry models on large datasets poses multi-faceted problems such as (a) high model-training time; (b) slow research iteration; and (c) poor eco-sustainability. As an alternative, data distillation approaches aim to synthesize terse data summaries, which can serve as effective drop-in replacements of the original dataset for scenarios like model training, inference, architecture search, etc. In this survey, we present a formal framework for data distillation, along with providing a detailed taxonomy of existing approaches. Additionally, we cover data distillation approaches for different data modalities, namely images, graphs, and user-item interactions (recommender systems), while also identifying current challenges and future research directions.

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