Swift Sampler: Efficient Learning of Sampler by 10 Parameters

• URL: http://arxiv.org/abs/2410.05578v1
• Date: Tue, 8 Oct 2024 00:26:29 GMT
• Title: Swift Sampler: Efficient Learning of Sampler by 10 Parameters
• Authors: Jiawei Yao, Chuming Li, Canran Xiao,
• Abstract summary: An effective data sampler assigns proper sampling probability for training data. textbfSS can be applied on large-scale data sets with high efficiency.
• Score: 5.595723519561982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data selection is essential for training deep learning models. An effective data sampler assigns proper sampling probability for training data and helps the model converge to a good local minimum with high performance. Previous studies in data sampling are mainly based on heuristic rules or learning through a huge amount of time-consuming trials. In this paper, we propose an automatic \textbf{swift sampler} search algorithm, \textbf{SS}, to explore automatically learning effective samplers efficiently. In particular, \textbf{SS} utilizes a novel formulation to map a sampler to a low dimension of hyper-parameters and uses an approximated local minimum to quickly examine the quality of a sampler. Benefiting from its low computational expense, \textbf{SS} can be applied on large-scale data sets with high efficiency. Comprehensive experiments on various tasks demonstrate that \textbf{SS} powered sampling can achieve obvious improvements (e.g., 1.5\% on ImageNet) and transfer among different neural networks. Project page: https://github.com/Alexander-Yao/Swift-Sampler.

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