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arXiv Machine Learning By Theresa Dahl Frehr, Niels Henrik Pontoppidan, Hiba Nassar, Tommy Sonne Alstr{\o}m

On What We Can Learn from Low-Resolution Data

Read the original on arXiv Machine Learning →

The paper investigates the impact of low‑resolution data on machine learning models, especially when high‑resolution data is scarce. It provides a theoretical analysis using Kullback‑Leibler divergence to quantify how data resolution affects a datapoint’s influence and derives bounds on information loss from downsampling. Empirical results with a vision transformer and a convolutional neural network show that incorporating low‑resolution data consistently improves performance in such settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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