Paper Reading AI Learner

Data Distillation: A Survey

2023-01-11 02:25:10
Noveen Sachdeva, Julian McAuley

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.

Abstract (translated)

URL

https://arxiv.org/abs/2301.04272

PDF

https://arxiv.org/pdf/2301.04272.pdf


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