wildphoton/RandConv
Code for ICLR2021 paper "Robust and Generalizable Visual Representation Learning via Random Convolutions"
This project helps machine learning practitioners improve the reliability and transferability of their image recognition models, especially when dealing with new or noisy visual data. By training models using a technique called Random Convolutions, it takes in image datasets (like digits or PACS images) and produces more robust visual representations. Data scientists and ML engineers working on computer vision tasks will find this useful.
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Use this if you need your image recognition models to perform consistently well even when faced with variations or corruptions in image data that weren't present during initial training.
Not ideal if you are looking for a general-purpose image labeling or object detection tool rather than a method for improving the foundational robustness of your visual models.
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Python
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Last pushed
May 10, 2021
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