Computer Science > Computer Vision and Pattern Recognition
[Submitted on 3 Mar 2023 (v1), last revised 6 Mar 2023 (this version, v2)]
Title:Unproportional mosaicing
View PDFAbstract:Data shift is a gap between data distribution used for training and data distribution encountered in the real-world. Data augmentations help narrow the gap by generating new data samples, increasing data variability, and data space coverage. We present a new data augmentation: Unproportional mosaicing (Unprop). Our augmentation randomly splits an image into various-sized blocks and swaps its content (pixels) while maintaining block sizes. Our method achieves a lower error rate when combined with other state-of-the-art augmentations.
Submission history
From: Vojtech Molek [view email][v1] Fri, 3 Mar 2023 16:55:44 UTC (1,432 KB)
[v2] Mon, 6 Mar 2023 08:58:43 UTC (139 KB)
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