Hi there 👋 I am Agnieszka!
- 💻 I’m currently working at Voicelab.ai (NLP R&D Team) and Gdańsk Univeristy of Technology
- 🤖 Check out our TRURL 2: a Polish Llama 2.0!
List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.
Hi there 👋 I am Agnieszka!
Nice work on the repo!
I wanted to suggest a couple of papers from ICCV 2019 you might want to add:
Occlusions for Effective Data Augmentation in Image Classification
https://arxiv.org/abs/1910.10651
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://arxiv.org/abs/1905.04899
Thanks for the great effort!
I want to suggest our WB augmentation to be added to your list.
Paper: https://arxiv.org/pdf/1912.06960.pdf
Github repo: https://github.com/mahmoudnafifi/WB_color_augmenter
Thanks!
Hi Agnieszka!
We're building a collection on the Data Augmentation for Code and have written a survey on this topic. Would love to see how we can bring our findings to this awesome repo 🤗
Cheers,
Terry
Hi,
Awesome data augmentation collections!
It would be great if you can add a new paper, "OnlineAugment: Online Data Augmentation with Less Domain Knowledge" from ECCV 2020. Here are the links:
Github repo: https://github.com/zhiqiangdon/online-augment
Paper link: https://arxiv.org/pdf/2007.09271.pdf
This paper proposes more general augmentations, which do not use image preprocessing operations. It is more automatic than AutoAugment and related as it does not require much domain knowledge to specify the operations and their ranges. Besides, it is very easy to use (one stage training in one GPU).
Thanks!
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