uda and UDA_pytorch
The PyTorch implementation is a community reimplementation of the original TensorFlow-based research codebase, making them competitors offering the same UDA algorithm in different deep learning frameworks rather than complementary tools.
About uda
google-research/uda
Unsupervised Data Augmentation (UDA)
This project helps machine learning practitioners significantly reduce the amount of labeled data needed to train high-performing models for tasks like classifying images or text. You feed it a small set of labeled examples alongside a larger pool of unlabeled data, and it outputs a highly accurate classification model. Data scientists, machine learning engineers, and researchers can use this to build effective models even when manual data labeling is expensive or time-consuming.
About UDA_pytorch
SanghunYun/UDA_pytorch
UDA(Unsupervised Data Augmentation) implemented by pytorch
This project helps improve the accuracy of text classification tasks, like sentiment analysis, even when you have very few labeled examples. It takes your existing text data, both labeled and unlabeled, and processes it to train a more robust text classification model. This is for data scientists, machine learning engineers, or researchers working with natural language processing who need to build high-performing text models with limited annotated data.
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