Learning to Sample the Most Useful Training Patches from Images

• URL: http://arxiv.org/abs/2011.12097v1
• Date: Tue, 24 Nov 2020 14:06:50 GMT
• Title: Learning to Sample the Most Useful Training Patches from Images
• Authors: Shuyang Sun, Liang Chen, Gregory Slabaugh, Philip Torr
• Abstract summary: We present a data-driven approach called PatchNet that learns to select the most useful patches from an image to construct a new training set. We show that our simple idea automatically selects informative samples out from a large-scale dataset, leading to a surprising 2.35dB generalisation gain in terms of PSNR.
• Score: 11.219920058662698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Some image restoration tasks like demosaicing require difficult training samples to learn effective models. Existing methods attempt to address this data training problem by manually collecting a new training dataset that contains adequate hard samples, however, there are still hard and simple areas even within one single image. In this paper, we present a data-driven approach called PatchNet that learns to select the most useful patches from an image to construct a new training set instead of manual or random selection. We show that our simple idea automatically selects informative samples out from a large-scale dataset, leading to a surprising 2.35dB generalisation gain in terms of PSNR. In addition to its remarkable effectiveness, PatchNet is also resource-friendly as it is applied only during training and therefore does not require any additional computational cost during inference.

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