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. 2023 Mar 22;18(9):1665–1672. doi: 10.1007/s11548-023-02864-8

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© The Author(s) 2023

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — Pictographical representation of TRandAugment. A video is segmented into T clips, and a random augmentation $t_{i}$ , sampled from a list of transforms $τ$ , is applied to clip i. The augmented clips are merged back to form a new video which is passed as input while training an end-to-end CNN+TCN network that predicts phases or steps