Toward Storage-Aware Learning with Compressed Data An Empirical Exploratory Study on JPEG
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915692074237952 |
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| author | Lee, Kichang Kim, Songkuk Park, JaeYeon Ko, JeongGil |
| author_facet | Lee, Kichang Kim, Songkuk Park, JaeYeon Ko, JeongGil |
| contents | On-device machine learning is often constrained by limited storage, particularly in continuous data collection scenarios. This paper presents an empirical study on storage-aware learning, focusing on the trade-off between data quantity and quality via compression. We demonstrate that naive strategies, such as uniform data dropping or one-size-fits-all compression, are suboptimal. Our findings further reveal that data samples exhibit varying sensitivities to compression, supporting the feasibility of a sample-wise adaptive compression strategy. These insights provide a foundation for developing a new class of storage-aware learning systems. The primary contribution of this work is the systematic characterization of this under-explored challenge, offering valuable insights that advance the understanding of storage-aware learning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_12833 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Storage-Aware Learning with Compressed Data An Empirical Exploratory Study on JPEG Lee, Kichang Kim, Songkuk Park, JaeYeon Ko, JeongGil Machine Learning Artificial Intelligence 68Txx I.2; I.4.2; E.4 On-device machine learning is often constrained by limited storage, particularly in continuous data collection scenarios. This paper presents an empirical study on storage-aware learning, focusing on the trade-off between data quantity and quality via compression. We demonstrate that naive strategies, such as uniform data dropping or one-size-fits-all compression, are suboptimal. Our findings further reveal that data samples exhibit varying sensitivities to compression, supporting the feasibility of a sample-wise adaptive compression strategy. These insights provide a foundation for developing a new class of storage-aware learning systems. The primary contribution of this work is the systematic characterization of this under-explored challenge, offering valuable insights that advance the understanding of storage-aware learning. |
| title | Toward Storage-Aware Learning with Compressed Data An Empirical Exploratory Study on JPEG |
| topic | Machine Learning Artificial Intelligence 68Txx I.2; I.4.2; E.4 |
| url | https://arxiv.org/abs/2508.12833 |