MarvinMartin24/MADA-PL
Multi-Adversarial Domain Adaptation (https://arxiv.org/abs/1809.02176) implementation in Pytorch-Lightning
This project helps machine learning engineers or researchers build image classification models that perform well even when the real-world images they encounter are significantly different from their training data. It takes labeled image datasets from one 'source' environment and unlabeled images from a 'target' environment, and outputs a more robust image classification model. This is for AI practitioners aiming to deploy models in varied real-world conditions.
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Use this if you need to improve the performance of your image classification models on target datasets that have a different visual style, lighting, or background compared to your original training data.
Not ideal if your classification task involves text or numerical data, or if your source and target image datasets are already visually very similar.
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18
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3
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 13, 2021
Commits (30d)
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